344 KiB
344 KiB
In [45]:
#IMPORTS import numpy as np import random import tensorflow as tf import tensorflow.keras as kr import tensorflow.keras.backend as K from tensorflow.keras.models import Model from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Flatten, Dense from tensorflow.keras.datasets import mnist import os import csv from scipy.spatial.distance import euclidean from sklearn.metrics import confusion_matrix from time import sleep from tqdm import tqdm import copy import numpy from sklearn.datasets import make_classification from sklearn.ensemble import RandomForestClassifier import pandas as pd import matplotlib.pyplot as plt import math import seaborn as sns from numpy.random import RandomState import scipy as scp from sklearn.model_selection import train_test_split from sklearn.compose import ColumnTransformer from sklearn.preprocessing import OneHotEncoder, LabelEncoder from keras.models import Sequential from keras.layers import Dense from keras import optimizers from keras.callbacks import EarlyStopping,ModelCheckpoint from keras.utils import to_categorical from keras import backend as K from itertools import product from sklearn.metrics import accuracy_score from sklearn.metrics import precision_score from sklearn.metrics import recall_score from sklearn.metrics import f1_score from sklearn.metrics import roc_auc_score from sklearn.metrics import confusion_matrix from sklearn import mixture from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt %matplotlib inline
In [46]:
feature_attacked = [3,5,8] rs = RandomState(92) #To reproduce the same results each time we run this notebook
In [47]:
#Load dataset into a pandas DataFrame activity = pd.read_csv("D:/explaineblity/activity_3_original.csv", sep=',')
In [48]:
to_drop = ['subject', 'timestamp', 'heart_rate','activityID'] activity.drop(axis=1, columns=to_drop, inplace=True)
In [49]:
display(activity.head())
motion | temp_hand | acceleration_16_x_hand | acceleration_16_y_hand | acceleration_16_z_hand | acceleration_6_x_hand | acceleration_6_y_hand | acceleration_6_z_hand | gyroscope_x_hand | gyroscope_y_hand | ... | acceleration_16_z_ankle | acceleration_6_x_ankle | acceleration_6_y_ankle | acceleration_6_z_ankle | gyroscope_x_ankle | gyroscope_y_ankle | gyroscope_z_ankle | magnetometer_x_ankle | magnetometer_y_ankle | magnetometer_z_ankle | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | n | 30.375 | 2.21530 | 8.27915 | 5.58753 | 2.24689 | 8.55387 | 5.77143 | -0.004750 | 0.037579 | ... | 0.095156 | 9.63162 | -1.76757 | 0.265761 | 0.002908 | -0.027714 | 0.001752 | -61.1081 | -36.8636 | -58.3696 |
1 | n | 30.375 | 2.29196 | 7.67288 | 5.74467 | 2.27373 | 8.14592 | 5.78739 | -0.171710 | 0.025479 | ... | -0.020804 | 9.58649 | -1.75247 | 0.250816 | 0.020882 | 0.000945 | 0.006007 | -60.8916 | -36.3197 | -58.3656 |
2 | n | 30.375 | 2.29090 | 7.14240 | 5.82342 | 2.26966 | 7.66268 | 5.78846 | -0.238241 | 0.011214 | ... | -0.059173 | 9.60196 | -1.73721 | 0.356632 | -0.035392 | -0.052422 | -0.004882 | -60.3407 | -35.7842 | -58.6119 |
3 | n | 30.375 | 2.21800 | 7.14365 | 5.89930 | 2.22177 | 7.25535 | 5.88000 | -0.192912 | 0.019053 | ... | 0.094385 | 9.58674 | -1.78264 | 0.311453 | -0.032514 | -0.018844 | 0.026950 | -60.7646 | -37.1028 | -57.8799 |
4 | n | 30.375 | 2.30106 | 7.25857 | 6.09259 | 2.20720 | 7.24042 | 5.95555 | -0.069961 | -0.018328 | ... | 0.095775 | 9.64677 | -1.75240 | 0.295902 | 0.001351 | -0.048878 | -0.006328 | -60.2040 | -37.1225 | -57.8847 |
5 rows × 40 columns
In [50]:
activity = pd.concat([activity,pd.get_dummies(activity['motion'], prefix='motion')],axis=1) activity.drop('motion', axis=1, inplace=True)
In [51]:
display(activity.head())
temp_hand | acceleration_16_x_hand | acceleration_16_y_hand | acceleration_16_z_hand | acceleration_6_x_hand | acceleration_6_y_hand | acceleration_6_z_hand | gyroscope_x_hand | gyroscope_y_hand | gyroscope_z_hand | ... | acceleration_6_y_ankle | acceleration_6_z_ankle | gyroscope_x_ankle | gyroscope_y_ankle | gyroscope_z_ankle | magnetometer_x_ankle | magnetometer_y_ankle | magnetometer_z_ankle | motion_n | motion_y | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 30.375 | 2.21530 | 8.27915 | 5.58753 | 2.24689 | 8.55387 | 5.77143 | -0.004750 | 0.037579 | -0.011145 | ... | -1.76757 | 0.265761 | 0.002908 | -0.027714 | 0.001752 | -61.1081 | -36.8636 | -58.3696 | 1 | 0 |
1 | 30.375 | 2.29196 | 7.67288 | 5.74467 | 2.27373 | 8.14592 | 5.78739 | -0.171710 | 0.025479 | -0.009538 | ... | -1.75247 | 0.250816 | 0.020882 | 0.000945 | 0.006007 | -60.8916 | -36.3197 | -58.3656 | 1 | 0 |
2 | 30.375 | 2.29090 | 7.14240 | 5.82342 | 2.26966 | 7.66268 | 5.78846 | -0.238241 | 0.011214 | 0.000831 | ... | -1.73721 | 0.356632 | -0.035392 | -0.052422 | -0.004882 | -60.3407 | -35.7842 | -58.6119 | 1 | 0 |
3 | 30.375 | 2.21800 | 7.14365 | 5.89930 | 2.22177 | 7.25535 | 5.88000 | -0.192912 | 0.019053 | 0.013374 | ... | -1.78264 | 0.311453 | -0.032514 | -0.018844 | 0.026950 | -60.7646 | -37.1028 | -57.8799 | 1 | 0 |
4 | 30.375 | 2.30106 | 7.25857 | 6.09259 | 2.20720 | 7.24042 | 5.95555 | -0.069961 | -0.018328 | 0.004582 | ... | -1.75240 | 0.295902 | 0.001351 | -0.048878 | -0.006328 | -60.2040 | -37.1225 | -57.8847 | 1 | 0 |
5 rows × 41 columns
In [52]:
class_label = [ 'motion_n', 'motion_y'] predictors = [a for a in activity.columns.values if a not in class_label] for p in predictors: activity[p].fillna(activity[p].mean(), inplace=True) display(predictors) for p in predictors: activity[p] = (activity[p]-activity[p].min()) / (activity[p].max() - activity[p].min()) activity[p].astype('float32')
['temp_hand', 'acceleration_16_x_hand', 'acceleration_16_y_hand', 'acceleration_16_z_hand', 'acceleration_6_x_hand', 'acceleration_6_y_hand', 'acceleration_6_z_hand', 'gyroscope_x_hand', 'gyroscope_y_hand', 'gyroscope_z_hand', 'magnetometer_x_hand', 'magnetometer_y_hand', 'magnetometer_z_hand', 'temp_chest', 'acceleration_16_x_chest', 'acceleration_16_y_chest', 'acceleration_16_z_chest', 'acceleration_6_x_chest', 'acceleration_6_y_chest', 'acceleration_6_z_chest', 'gyroscope_x_chest', 'gyroscope_y_chest', 'gyroscope_z_chest', 'magnetometer_x_chest', 'magnetometer_y_chest', 'magnetometer_z_chest', 'temp_ankle', 'acceleration_16_x_ankle', 'acceleration_16_y_ankle', 'acceleration_16_z_ankle', 'acceleration_6_x_ankle', 'acceleration_6_y_ankle', 'acceleration_6_z_ankle', 'gyroscope_x_ankle', 'gyroscope_y_ankle', 'gyroscope_z_ankle', 'magnetometer_x_ankle', 'magnetometer_y_ankle', 'magnetometer_z_ankle']
In [53]:
activity = activity.to_numpy()
In [54]:
activity.shape
Out[54]:
(1942872, 41)
In [55]:
X_train, X_test, y_train, y_test = train_test_split(activity[:,:-2],activity[:,-2:], test_size=0.07, random_state=rs)
In [56]:
#begin federated earlystopping = EarlyStopping(monitor = 'val_loss', min_delta = 0.01, patience = 50, verbose = 1, baseline = 2, restore_best_weights = True) checkpoint = ModelCheckpoint('test.h8', monitor='val_loss', mode='min', save_best_only=True, verbose=1) model = Sequential() model.add(Dense(70, input_dim=39, activation='relu')) model.add(Dense(50, activation='relu')) model.add(Dense(50, activation='relu')) model.add(Dense(2, activation='softmax')) #sgd = optimizers.SGD(learning_rate=0.0001, momentum=0.9, nesterov=True) model.compile(loss='categorical_crossentropy', optimizer='adam',metrics=['accuracy']) # def train_shard(i): history = model.fit(X_train, y_train, epochs=2, validation_data=(X_test, y_test), callbacks = [checkpoint, earlystopping], shuffle=True) # return history # for i in range(len(shard1_traintest)): # train_shard(i) #get_3rd_layer_output = K.function([model.layers[0].input], # [model.layers[2].output]) #layer_output = get_3rd_layer_output(shard_traintest[i]["X_train"])[0]
Train on 1806870 samples, validate on 136002 samples Epoch 1/2
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accuracy: 0.99 - ETA: 0s - loss: 0.0230 - accuracy: 0.99 - ETA: 0s - loss: 0.0230 - accuracy: 0.99 - ETA: 0s - loss: 0.0229 - accuracy: 0.99 - ETA: 0s - loss: 0.0229 - accuracy: 0.99 - ETA: 0s - loss: 0.0230 - accuracy: 0.99 - ETA: 0s - loss: 0.0229 - accuracy: 0.99 - ETA: 0s - loss: 0.0229 - accuracy: 0.99 - 37s 20us/step - loss: 0.0229 - accuracy: 0.9923 - val_loss: 0.0129 - val_accuracy: 0.9955 Epoch 00002: val_loss improved from 0.03567 to 0.01290, saving model to test.h8
In [57]:
model.summary()
Model: "sequential_4" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_13 (Dense) (None, 70) 2800 _________________________________________________________________ dense_14 (Dense) (None, 50) 3550 _________________________________________________________________ dense_15 (Dense) (None, 50) 2550 _________________________________________________________________ dense_16 (Dense) (None, 2) 102 ================================================================= Total params: 9,002 Trainable params: 9,002 Non-trainable params: 0 _________________________________________________________________
In [58]:
#AUXILIARY METHODS FOR FEDERATED LEARNING # RETURN INDICES TO LAYERS WITH WEIGHTS AND BIASES def trainable_layers(model): return [i for i, layer in enumerate(model.layers) if len(layer.get_weights()) > 0] # RETURN WEIGHTS AND BIASES OF A MODEL def get_parameters(model): weights = [] biases = [] index = trainable_layers(model) for i in index: weights.append(copy.deepcopy(model.layers[i].get_weights()[0])) biases.append(copy.deepcopy(model.layers[i].get_weights()[1])) return weights, biases # SET WEIGHTS AND BIASES OF A MODEL def set_parameters(model, weights, biases): index = trainable_layers(model) for i, j in enumerate(index): model.layers[j].set_weights([weights[i], biases[i]]) # DEPRECATED: RETURN THE GRADIENTS OF THE MODEL AFTER AN UPDATE def get_gradients(model, inputs, outputs): """ Gets gradient of model for given inputs and outputs for all weights""" grads = model.optimizer.get_gradients(model.total_loss, model.trainable_weights) symb_inputs = (model._feed_inputs + model._feed_targets + model._feed_sample_weights) f = K.function(symb_inputs, grads) x, y, sample_weight = model._standardize_user_data(inputs, outputs) output_grad = f(x + y + sample_weight) w_grad = [w for i,w in enumerate(output_grad) if i%2==0] b_grad = [w for i,w in enumerate(output_grad) if i%2==1] return w_grad, b_grad # RETURN THE DIFFERENCE OF MODELS' WEIGHTS AND BIASES AFTER AN UPDATE # NOTE: LEARNING RATE IS APPLIED, SO THE UPDATE IS DIFFERENT FROM THE # GRADIENTS. IN CASE VANILLA SGD IS USED, THE GRADIENTS ARE OBTAINED # AS (UPDATES / LEARNING_RATE) def get_updates(model, inputs, outputs, batch_size, epochs): w, b = get_parameters(model) #model.train_on_batch(inputs, outputs) model.fit(inputs, outputs, batch_size=batch_size, epochs=epochs, verbose=0) w_new, b_new = get_parameters(model) weight_updates = [old - new for old,new in zip(w, w_new)] bias_updates = [old - new for old,new in zip(b, b_new)] return weight_updates, bias_updates # UPDATE THE MODEL'S WEIGHTS AND PARAMETERS WITH AN UPDATE def apply_updates(model, eta, w_new, b_new): w, b = get_parameters(model) new_weights = [theta - eta*delta for theta,delta in zip(w, w_new)] new_biases = [theta - eta*delta for theta,delta in zip(b, b_new)] set_parameters(model, new_weights, new_biases) # FEDERATED AGGREGATION FUNCTION def aggregate(n_layers, n_peers, f, w_updates, b_updates): agg_w = [f([w_updates[j][i] for j in range(n_peers)], axis=0) for i in range(n_layers)] agg_b = [f([b_updates[j][i] for j in range(n_peers)], axis=0) for i in range(n_layers)] return agg_w, agg_b # SOLVE NANS def nans_to_zero(W, B): W0 = [np.nan_to_num(w, nan=0.0, posinf=0.0, neginf=0.0) for w in W] B0 = [np.nan_to_num(b, nan=0.0, posinf=0.0, neginf=0.0) for b in B] return W0, B0 def build_forest(X,y): clf=RandomForestClassifier(n_estimators=1000, max_depth=7, random_state=0, verbose = 1) clf.fit(X,y) return clf
In [59]:
def scan_wrong(forest_predictions, FL_predict1, forest , y_test_local, X_test_local): sum_feature_improtance= 0 overal_wrong_feature_importance = 0 counter = 0 second_counter = 0 never_seen = 0 avr_wrong_importance = 0 counter1 = 0 for i in range (len(FL_predict1)): if(FL_predict1[i][0] < 0.5): FL_predict1[i][0] = 0 FL_predict1[i][1] = 1 if(FL_predict1[i][0] >= 0.5): FL_predict1[i][0] = 1 FL_predict1[i][1] = 0 for i in range (len(FL_predict1)): i_tree = 0 # print(i) if (FL_predict1[i][0] != y_test_local[i][0]): counter1+=1 # print(i) # print("the test sample number ",i ," have been niss classified by the blackbox" ) for tree_in_forest in forest.estimators_: temp = forest.estimators_[i_tree].predict([X_test_local[i]]) i_tree = i_tree + 1 inttemp = temp[0].astype(int) if(FL_predict1[i][0] == inttemp[0]): sum_feature_improtance = sum_feature_improtance + tree_in_forest.feature_importances_ counter = counter + 1 if(counter>0): ave_feature_importence = sum_feature_improtance/counter overal_wrong_feature_importance = ave_feature_importence + overal_wrong_feature_importance second_counter = second_counter + 1 # print(ave_feature_importence) # print("numbers of the trees predect the wrong predection as the blackbox is ", counter) counter = 0 sum_feature_improtance = 0 # print("------------------------------------------------------------------------------------") else: if(FL_predict1[i][0] != y_test_local[i][0]): # print("the test sample number ", i," never have been miss classified by the forest.") never_seen = never_seen +1 if(second_counter>0): # print(second_counter) # print("the number of sampels that was miss classifed by the blackbox and classified correctly by the all forest is", never_seen) # print(overal_wrong_feature_importance) avr_wrong_importance = overal_wrong_feature_importance / second_counter # print("the average wrong dessition cosed by the feature", avr_wrong_importance) # print("=====================================================================================") print("the number of miss classified sampels is ", counter1) return forest.feature_importances_
In [60]:
def attack_data(inputs, feature_attacked): z=0 C=0 z=inputs.max(axis = 0) C=inputs.min(axis = 0) for i in range(len(inputs)): for j in range(len(inputs[0])): inputs[i][j]= random.uniform(z[j], C[j]) # inputs[i][feature_attacked[1]]= random.uniform(z[feature_attacked[1]], C[feature_attacked[1]]) # inputs[i][feature_attacked[1]]= random.uniform(z[feature_attacked[1]], C[feature_attacked[1]]) # inputs[i][feature_attacked] = random.randrange(z[feature_attacked]+1) # print(X_test_attacked[i][att]) # if(X_test_attacked[i][att] == X_test[i][att]): # feat_same = feat_same + 1 return inputs
In [61]:
trainable_layers(model)
Out[61]:
[0, 1, 2, 3]
In [62]:
get_updates(model, X_train, y_train, 32, 2)
Out[62]:
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-5.26163459e-01, ..., -4.11394715e-01, 0.00000000e+00, 2.40460098e-01], [ 2.49932185e-01, -3.12010467e-01, -4.72681373e-01, ..., 6.11434951e-02, 0.00000000e+00, -8.85864496e-02], ..., [-1.02717876e-02, 4.91040945e-02, 2.08511353e-01, ..., 2.92169333e-01, 3.78781557e-03, -7.43160397e-02], [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ..., 0.00000000e+00, 0.00000000e+00, 0.00000000e+00], [-2.40534097e-01, -1.12464696e-01, -6.61374629e-02, ..., -2.46955007e-01, 2.02164054e-04, -1.38975129e-01]], dtype=float32), array([[-3.04001868e-01, 3.04008663e-01], [-3.18321645e-01, 3.18326294e-01], [-6.49204254e-02, 6.49255514e-02], [ 2.38253117e-01, -2.38266587e-01], [-2.33515322e-01, 2.33533710e-01], [ 2.04552054e-01, -2.04541385e-01], [-2.16077894e-01, 2.16096789e-01], [-9.29667503e-02, 9.29654241e-02], [ 8.22023153e-02, -8.21979046e-02], [-1.02218628e-01, 1.02235526e-01], [-2.78488606e-01, 2.78504372e-01], [-2.50967979e-01, 2.50985503e-01], [ 6.83438182e-02, -6.83349371e-02], [ 1.24650508e-01, -1.24644592e-01], [ 6.33001328e-05, -6.16163015e-05], [ 1.41896963e-01, -1.41895413e-01], [-1.02908731e-01, 1.02926940e-01], [ 5.35694361e-02, -5.35876751e-02], [ 8.15955400e-02, -8.15925598e-02], [-1.03019953e-01, 1.03019953e-01], [-1.25430942e-01, 1.25459164e-01], [-3.38193893e-01, 3.38200092e-01], [-1.09561086e-02, 1.09702498e-02], [-2.82736778e-01, 2.82758176e-01], [-4.44638729e-01, 4.44639683e-01], [-6.57172203e-02, 6.57169819e-02], [-1.72389388e-01, 1.72392488e-01], [-5.41939139e-02, 5.42033315e-02], [-5.90079725e-02, 5.90344965e-02], [ 3.66447330e-01, -3.66433740e-01], [-2.08910614e-01, 2.08919704e-01], [-2.05386773e-01, 2.05394983e-01], [ 2.22557023e-01, -2.22551197e-01], [ 1.47694349e-01, -1.47691488e-01], [-1.98568344e-01, 1.98586702e-01], [ 3.86301279e-02, -3.86058390e-02], [-3.03680480e-01, 3.03690165e-01], [ 1.28941819e-01, -1.28929913e-01], [-1.06625021e-01, 1.06641471e-01], [ 2.11793751e-01, -2.11783320e-01], [-1.20612228e+00, 1.20613194e+00], [ 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0.0000000e+00, -6.1750807e-02, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 5.8094569e-02, 1.2169727e-02, 1.9261871e-10, 0.0000000e+00, -7.9979539e-02, 0.0000000e+00, 0.0000000e+00, 7.6093122e-02, 0.0000000e+00, 0.0000000e+00, 1.6243661e-02, 0.0000000e+00, -9.8505989e-03, 0.0000000e+00, 0.0000000e+00, -4.3590821e-02], dtype=float32), array([-0.10964979, 0.1273394 , 0.05826145, 0.11250992, -0.00930649, 0.08082695, -0.10440034, 0.02672271, 0.14781642, 0.27572772, -0.0397696 , 0.18053436, 0. , -0.04786159, 0. , 0.15616408, 0.0424803 , 0. , -0.0375067 , -0.00756753, 0.01335342, -0.08301416, -0.07382136, 0.02766102, -0.21604276, 0.04904766, -0.00283363, 0.01198358, 0.17403054, -0.08457427, 0.06056517, -0.00864101, 0.02029612, 0.12778968, 0.14824837, 0.17251332, 0.03519725, 0.05309688, 0.1472145 , -0.18282993, 0.10138815, 0.01851342, 0.03132945, 0. , -0.19095713, 0.07761121, 0.17995 , 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In [63]:
W = get_parameters(model)[0] B = get_parameters(model)[1]
In [64]:
#AUXILIARY METHODS FOR FL INSPECTION # TRANSFORM ALL WEIGHT TENSORS TO 1D ARRAY def flatten_weights(w_in): h = w_in[0].reshape(-1) for w in w_in[1:]: h = np.append(h, w.reshape(-1)) return h # TRANSFORM ALL BIAS TENSORS TO 1D ARRAY def flatten_biases(b_in): h = b_in[0].reshape(-1) for b in b_in[1:]: h = np.append(h, b.reshape(-1)) return h # TRANSFORM WEIGHT AND BIAS TENSORS TO 1D ARRAY def flatten_parameters(w_in, b_in): w = flatten_weights(w_in) b = flatten_biases(b_in) return w, b # COMPUTE EUCLIDEAN DISTANCE OF WEIGHTS def dist_weights(w_a, w_b): wf_a = flatten_weights(w_a) wf_b = flatten_weights(w_b) return euclidean(wf_a, wf_b) # COMPUTE EUCLIDEAN DISTANCE OF BIASES def dist_biases(b_a, b_b): bf_a = flatten_biases(b_a) bf_b = flatten_biases(b_b) return euclidean(bf_a, bf_b) # COMPUTE EUCLIDEAN DISTANCE OF WEIGHTS AND BIASES def dist_parameters(w_a, b_a, w_b, b_b): wf_a, bf_a = flatten_parameters(w_a, b_a) wf_b, bf_b = flatten_parameters(w_b, b_b) return euclidean(np.append(wf_a, bf_a), np.append(wf_b, bf_b))
In [65]:
len(W[0])
Out[65]:
39
In [66]:
# BASELINE SCENARIO #buid the model as base line for the shards (sequential) # Number of peers #accordin to what we need n_peers = 100 ss = int(len(X_train)/n_peers) inputs_in = X_train[0*ss:0*ss+ss] outputs_in = y_train[0*ss:0*ss+ss] def build_model(X_t, y_t): model = Sequential() model.add(Dense(70, input_dim=39, activation='relu')) model.add(Dense(64, activation='relu')) model.add(Dense(50, activation='relu')) model.add(Dense(2, activation='softmax')) #sgd = optimizers.SGD(learning_rate=0.0001, momentum=0.9, nesterov=True) model.compile(loss='categorical_crossentropy', optimizer='adam',metrics=['accuracy']) model.fit(X_t, y_t, #inputs_in, #outputs_in, # X_train, # y_train, batch_size=32, epochs=100, verbose=1, validation_data=((X_test, y_test))) return model # model = build_model(inputs_in, outputs_in)
In [67]:
display(model.summary())
Model: "sequential_4" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_13 (Dense) (None, 70) 2800 _________________________________________________________________ dense_14 (Dense) (None, 50) 3550 _________________________________________________________________ dense_15 (Dense) (None, 50) 2550 _________________________________________________________________ dense_16 (Dense) (None, 2) 102 ================================================================= Total params: 9,002 Trainable params: 9,002 Non-trainable params: 0 _________________________________________________________________
None
In [68]:
# predict probabilities for test set yhat_probs = model.predict(X_test, verbose=0) # predict crisp classes for test set yhat_classes = model.predict_classes(X_test, verbose=0)
In [69]:
# accuracy: (tp + tn) / (p + n) accuracy = accuracy_score(np.argmax(y_test, axis=1), np.argmax(model.predict(X_test), axis=1)) print('Accuracy: %f' % accuracy) # precision tp / (tp + fp) precision = precision_score(np.argmax(y_test, axis=1), np.argmax(model.predict(X_test), axis=1)) print('Precision: %f' % precision) # recall: tp / (tp + fn) recall = recall_score(np.argmax(y_test, axis=1), np.argmax(model.predict(X_test), axis=1)) print('Recall: %f' % recall) # f1: 2 tp / (2 tp + fp + fn) f1 = f1_score(np.argmax(y_test, axis=1), np.argmax(model.predict(X_test), axis=1)) print('F1 score: %f' % f1)
Accuracy: 0.998316 Precision: 0.998423 Recall: 0.998711 F1 score: 0.998567
In [70]:
# ROC AUC # auc = roc_auc_score(shard1_traintest[i]["y_test"], yhat_probs) # print('ROC AUC: %f' % auc) # confusion matrix mat = confusion_matrix(np.argmax(y_test, axis=1), np.argmax(model.predict(X_test), axis=1)) display(mat) plt.matshow(mat); plt.colorbar() plt.show()
array([[55991, 126], [ 103, 79782]], dtype=int64)
In [71]:
def savecsv(lists, filename): #print lists if os.path.isfile(filename): os.remove(filename) with open(filename, 'a') as csvfile: w = csv.DictWriter(csvfile, lists.keys()) w.writeheader() w.writerow(lists) # fwriter = csv.writer(csvfile, delimiter=',',lineterminator='\n') # fwriter.writerows(lists) csvfile.close() # import csv # my_dict = {"test": 1, "testing": 2} # with open('mycsvfile.csv', 'wb') as f: # Just use 'w' mode in 3.x # w = csv.DictWriter(f, my_dict.keys()) # w.writeheader() # w.writerow(my_dict)
In [72]:
FI_dic1= {0:[],1:[],2:[],3:[],4:[],5:[],6:[],7:[],8:[],9:[]}#,10:[],11:[],12:[],13:[],14:[],15:[],16:[],17:[],18:[],19:[],20:[], # 21:[],22:[],23:[],24:[],25:[],26:[],27:[],28:[],29:[]} FI_dic2= {0:[],1:[],2:[],3:[],4:[],5:[],6:[],7:[],8:[],9:[]}#,10:[],11:[],12:[],13:[],14:[],15:[],16:[],17:[],18:[],19:[],20:[], # 21:[],22:[],23:[],24:[],25:[],26:[],27:[],28:[],29:[]} # FI_dic3= {0:[],1:[],2:[],3:[],4:[],5:[],6:[],7:[],8:[],9:[],10:[],11:[],12:[],13:[],14:[],15:[],16:[],17:[],18:[],19:[],20:[], # 21:[],22:[],23:[],24:[],25:[],26:[],27:[],28:[],29:[]} # FI_dic4= {0:[],1:[],2:[],3:[],4:[],5:[],6:[],7:[],8:[],9:[],10:[],11:[],12:[],13:[],14:[],15:[],16:[],17:[],18:[],19:[],20:[], # 21:[],22:[],23:[],24:[],25:[],26:[],27:[],28:[],29:[]} # FI_dic5= {0:[],1:[],2:[],3:[],4:[],5:[],6:[],7:[],8:[],9:[],10:[],11:[],12:[],13:[],14:[],15:[],16:[],17:[],18:[],19:[],20:[], # 21:[],22:[],23:[],24:[],25:[],26:[],27:[],28:[],29:[]} # FI_dic6= {0:[],1:[],2:[],3:[],4:[],5:[],6:[],7:[],8:[],9:[],10:[],11:[],12:[],13:[],14:[],15:[],16:[],17:[],18:[],19:[],20:[], # 21:[],22:[],23:[],24:[],25:[],26:[],27:[],28:[],29:[]} # FI_dic7= {0:[],1:[],2:[],3:[],4:[],5:[],6:[],7:[],8:[],9:[],10:[],11:[],12:[],13:[],14:[],15:[],16:[],17:[],18:[],19:[],20:[], # 21:[],22:[],23:[],24:[],25:[],26:[],27:[],28:[],29:[]} # FI_dic8= {0:[],1:[],2:[],3:[],4:[],5:[],6:[],7:[],8:[],9:[],10:[],11:[],12:[],13:[],14:[],15:[],16:[],17:[],18:[],19:[],20:[], # 21:[],22:[],23:[],24:[],25:[],26:[],27:[],28:[],29:[]} # FI_dic9= {0:[],1:[],2:[],3:[],4:[],5:[],6:[],7:[],8:[],9:[],10:[],11:[],12:[],13:[],14:[],15:[],16:[],17:[],18:[],19:[],20:[], # 21:[],22:[],23:[],24:[],25:[],26:[],27:[],28:[],29:[]} # FI_dic10= {0:[],1:[],2:[],3:[],4:[],5:[],6:[],7:[],8:[],9:[],10:[],11:[],12:[],13:[],14:[],15:[],16:[],17:[],18:[],19:[],20:[], # 21:[],22:[],23:[],24:[],25:[],26:[],27:[],28:[],29:[]} # FI_dic11= {0:[],1:[],2:[],3:[],4:[],5:[],6:[],7:[],8:[],9:[],10:[],11:[],12:[],13:[],14:[],15:[],16:[],17:[],18:[],19:[],20:[], # 21:[],22:[],23:[],24:[],25:[],26:[],27:[],28:[],29:[],30:[]} dic = {0:[],1:[],2:[],3:[],4:[],5:[],6:[],7:[],8:[],9:[],10:[],11:[],12:[],13:[],14:[],15:[],16:[],17:[]}
In [73]:
# FL Begain # TESTBED ##################### # SYSTEM PARAMETERS # ##################### for c in range(1): number_attackers = c # threshold = 0.011 threshold =0.00034 counter = 0 peers_selected=random.sample(range(n_peers), number_attackers+1) scaner = peers_selected[0] mal = peers_selected[1 :] # Percentage and number of peers participating at each global training epoch percentage_participants = 1.0 n_participants = int(n_peers * percentage_participants) # Number of global training epochs n_rounds = 10 start_attack_round = 4 end_attack_round = 7 # Number of local training epochs per global training epoch n_local_rounds = 5 # Local batch size local_batch_size = 32 # Local learning rate local_lr = 0.001 # Global learning rate or 'gain' model_substitution_rate = 1.0 # Attack detection / prevention mechanism = {None, 'distance', 'median', 'accuracy', 'krum'} discard_outliers = None # Used in 'dist' attack detection, defines how far the outliers are (1.5 is a typical value) tau = 1.5 # Used in 'accuracy' attack detection, defines the error margin for the accuracy improvement sensitivity = 0.05 # Used in 'krum' attack detection, defines how many byzantine attackers we want to defend against tolerance=4 # Prevent suspicious peers from participating again, only valid for 'dist' and 'accuracy' ban_malicious = False # Clear nans and infinites in model updates clear_nans = True number_for_threshold1 = numpy.empty(20, dtype=float) number_for_threshold2 = numpy.empty(20, dtype=float) for r in range(len(number_for_threshold1)): number_for_threshold1[r] = 0 number_for_threshold2[r] = 0 ######################## # ATTACK CONFIGURATION # ######################## # Percentage of malicious peers r_malicious_peers = 0.0 # Number of malicious peers (absolute or relative to total number of peers) n_malicious_peers = int(n_peers * r_malicious_peers) #n_malicious_peers = 1 # Malicious peers malicious_peer = range(n_malicious_peers) # Target for coalitions common_attack_target = [4,7] # Target class of the attack, per each malicious peer malicious_targets = dict([(p, t) for p,t in zip(malicious_peer, [common_attack_target]*n_malicious_peers)]) # Boosting parameter per each malicious peer common_malicious_boost = 12 malicious_boost = dict([(p, b) for p,b in zip(malicious_peer, [common_malicious_boost]*n_malicious_peers)]) ########### # METRICS # ########### metrics = {'accuracy': [], 'atk_effectivity': [], 'update_distances': [], 'outliers_detected': [], 'acc_no_target': []} #################################### # MODEL AND NETWORK INITIALIZATION # #################################### inputs = X_train[0*ss:0*ss+ss] outputs = y_train[0*ss:0*ss+ss] global_model = build_model(inputs,outputs) n_layers = len(trainable_layers(global_model)) print('Initializing network.') sleep(1) network = [] for i in tqdm(range(n_peers)): ss = int(len(X_train)/n_peers) inputs = X_train[i*ss:i*ss+ss] outputs = y_train[i*ss:i*ss+ss] # network.append(build_model(inputs, outputs)) network.append(global_model) banned_peers = set() ################## # BEGIN TRAINING # ################## for t in range(n_rounds): print(f'Round {t+1}.') sleep(1) ## SERVER SIDE ################################################################# # Fetch global model parameters global_weights, global_biases = get_parameters(global_model) if clear_nans: global_weights, global_biases = nans_to_zero(global_weights, global_biases) # Initialize peer update lists network_weight_updates = [] network_bias_updates = [] # Selection of participant peers in this global training epoch if ban_malicious: good_peers = list([p for i,p in enumerate(network) if i not in banned_peers]) n_participants = n_participants if n_participants <= len(good_peers) else int(len(good_peers) * percentage_participants) participants = random.sample(list(enumerate(good_peers)), n_participants) else: participants = random.sample(list(enumerate(network)),n_participants) ################################################################################ ## CLIENT SIDE ################################################################# for i, local_model in tqdm(participants): # Update local model with global parameters set_parameters(local_model, global_weights, global_biases) # Initialization of user data ss = int(len(X_train)/n_peers) inputs = X_train[i*ss:i*ss+ss] outputs = y_train[i*ss:i*ss+ss] # print("worker number ", i," from ", n_peers) # print(" number of data in worker ", i ," is ", len(inputs)) # do the forest here # counter = counter+1 if(i == scaner): X_train_local, X_test_local, y_train_local, y_test_local = train_test_split(inputs,outputs, test_size=0.7, random_state=rs) inputs = X_train_local outputs = y_train_local if(t == 0): forest = build_forest(X_train_local,y_train_local) forest_predictions = forest.predict(X_test_local) acc_forest = np.mean([t==p for t,p in zip(y_test_local, forest_predictions)]) # imp = forest.feature_importances_ # FI_dic1[t] = imp FL_predict1 = global_model.predict(X_test_local) imp = scan_wrong(forest_predictions, FL_predict1, forest , y_test_local, X_test_local) FI_dic1[t] = imp # if(t > 0): # different_rouneds = FI_dic1[t-1] - FI_dic1[t] # different_rouneds = abs(different_rouneds) # number_for_threshold=0 # print("lenght of different ",len(different_rouneds)) # for H in range(len(different_rouneds)): # number_for_threshold1[t] = number_for_threshold1[t] + different_rouneds[H] # number_for_threshold = number_for_threshold1[t] - number_for_threshold1[t-1] # if(t > 1): # print(number_for_threshold) # dic[c].append(abs(number_for_threshold)) # if(abs(number_for_threshold)>threshold): # print("---------------------------------------------------------") # print("attack happened , in the round before which is ", t+1) # print("from peer ", i) # print(different_rouneds) # print(number_for_threshold) # print("---------------------------------------------------------") # number_for_threshold1 = numpy.empty(19, dtype=float) # for i in range(len(number_for_threshold1)): # number_for_threshold1[i] = 0 # for j in range(len(FI_dic1)-1): # number_for_threshold1 = numpy.empty(19, dtype=float) # different_rouneds = FI_dic2[j] - FI_dic2[j] # different_rouneds = abs(different_rouneds) # # number_for_threshold=0 # for i in range(len(different_rouneds)): # number_for_threshold1[j] = number_for_threshold1[j] + different_rouneds[i] # if(i == 12): # X_train_local, X_test_local, y_train_local, y_test_local = train_test_split(inputs,outputs, test_size=0.7, random_state=rs) # inputs = X_train_local # outputs = y_train_local # forest = build_forest(X_train_local,y_train_local) # forest_predictions = forest.predict(X_test_local) # acc_forest = np.mean([t==p for t,p in zip(y_test_local, forest_predictions)]) # imp = forest.feature_importances_ # # FI_dic2[t] = imp # # FL_predict2 = global_model.predict(X_test_local) # FL_predict2 = global_model.predict(X_test_local) # imp = scan_wrong(forest_predictions, FL_predict2, forest , y_test_local, X_test_local) # FI_dic2[t] = imp # if(t > 0): # different_rouneds = FI_dic2[t-1] - FI_dic2[t] # different_rouneds = abs(different_rouneds) # number_for_threshold=0 # for H in range(len(different_rouneds)): # number_for_threshold2[t] = number_for_threshold2[t] + different_rouneds[H] # number_for_threshold = number_for_threshold1[t] - number_for_threshold1[t-1] # if(t>1): # if(abs(number_for_threshold)>threshold): # print("---------------------------------------------------------") # print("attack happened, in the round before which is ", t) # print("from peer ", i) # print(different_rouneds) # print(number_for_threshold) # print("---------------------------------------------------------") # Local model update if i in malicious_peer: # Malicious peer # Modify target class labels outputs = poison(outputs, malicious_targets[i][0], malicious_targets[i][1]) # Train local model with benign and malicious data local_weight_updates, local_bias_updates = get_updates(local_model, inputs, outputs, local_batch_size, n_local_rounds) # Boost update local_weight_updates, local_bias_updates = boosting(local_weight_updates, local_bias_updates, malicious_boost[i]) if clear_nans: local_weight_updates, local_bias_updates = nans_to_zero(local_weight_updates, local_bias_updates) #here atttack done by Rami elif(t+1>=start_attack_round and t+1<=end_attack_round): if (i in mal): # print("peer ",i, "round", t) print("I am peer ",i,"I started the attack, at round", t+1) #attack inputs = attack_data(inputs, feature_attacked) local_weight_updates, local_bias_updates = get_updates(local_model, inputs, outputs, local_batch_size, n_local_rounds) if clear_nans: local_weight_updates, local_bias_updates = nans_to_zero(local_weight_updates, local_bias_updates) else: # Benign peer # Train local model local_weight_updates, local_bias_updates = get_updates(local_model, inputs, outputs, local_batch_size, n_local_rounds) if clear_nans: local_weight_updates, local_bias_updates = nans_to_zero(local_weight_updates, local_bias_updates) # Send updates to the server network_weight_updates.append(local_weight_updates) network_bias_updates.append(local_bias_updates) ## END OF CLIENT SIDE ########################################################## ###################################### # SERVER SIDE AGGREGATION MECHANISMS # ###################################### # No detection of outliers if discard_outliers == None: # Aggregate client updates aggregated_weights, aggregated_biases = aggregate(n_layers, n_participants, np.mean, network_weight_updates, network_bias_updates) if clear_nans: aggregated_weights, aggregated_biases = nans_to_zero(aggregated_weights, aggregated_biases) # Apply updates to global model apply_updates(global_model, model_substitution_rate, aggregated_weights, aggregated_biases) # Detection of outliers via distance metric elif discard_outliers == 'distance': # Compute the provisional aggregate prov_agg_w, prov_agg_b = aggregate(n_layers, n_participants, np.mean, network_weight_updates, network_bias_updates) # Compute distances and IQR of individual updates to the provisional aggregate distances = [dist_weights(prov_agg_w, w_i) for w_i in network_weight_updates] q1 = np.percentile(distances, 25) q3 = np.percentile(distances, 75) iqr = q3 - q1 low = q1 - tau * iqr high = q3 + tau * iqr # Discard outliers good_updates = [i for i,v in enumerate(distances) if low <= v <= high] agg_participants = len(good_updates) network_weight_updates = [w for i,w in enumerate(network_weight_updates) if i in good_updates] network_bias_updates = [b for i,b in enumerate(network_bias_updates) if i in good_updates] bad_participants = [i for i in range(n_participants) if i not in good_updates] bad_participants = [participants[i][0] for i in bad_participants] # Flag offenders banned_peers.update(bad_participants) metrics['outliers_detected'].append(bad_participants) # Compute definitive update aggregated_weights, aggregated_biases = aggregate(n_layers, agg_participants, np.mean, network_weight_updates, network_bias_updates) if clear_nans: aggregated_weights, aggregated_biases = nans_to_zero(aggregated_weights, aggregated_biases) # Apply update apply_updates(global_model, model_substitution_rate, aggregated_weights, aggregated_biases) #Detection via GMM #elif discard_otliers == 'gmm': #flatten_parameters = [flatten_parameters(w, b)[ #for w,b in zip(network_weight_updates, network_bias_updates) #]] # Detection of outliers via accuracy metrics elif discard_outliers == 'accuracy': if t == 0: # In the first epoch, all contributions are accepted aggregated_weights, aggregated_biases = aggregate(n_layers, n_participants, np.mean, network_weight_updates, network_bias_updates) if clear_nans: aggregated_weights, aggregated_biases = nans_to_zero(aggregated_weights, aggregated_biases) apply_updates(global_model, model_substitution_rate, aggregated_weights, aggregated_biases) else: # Check the change in accuracy for every contribution test_accuracies = [] previous_epoch_global_accuracy = metrics['accuracy'][-1] for k in range(len(local_weight_updates)): test_model = build_model(local_lr) set_parameters(test_model, global_weights, global_biases) apply_updates(test_model, model_substitution_rate, network_weight_updates[k], network_bias_updates[k]) _, test_accuracy = test_model.evaluate(x_test, y_test, verbose=0) test_accuracies.append(test_accuracy - previous_epoch_global_accuracy) # An update is good if it improves (with some margin) the accuracy of the # global model good_updates = [i for i,v in enumerate(test_accuracies) if v + sensitivity >= 0.0] agg_participants = len(good_updates) network_weight_updates = [w for i,w in enumerate(network_weight_updates) if i in good_updates] network_bias_updates = [b for i,b in enumerate(network_bias_updates) if i in good_updates] bad_participants = [i for i in range(n_participants) if i not in good_updates] bad_participants = [participants[i][0] for i in bad_participants] # Flag offenders banned_peers.update(bad_participants) metrics['outliers_detected'].append(bad_participants) # Compute definitive update aggregated_weights, aggregated_biases = aggregate(n_layers, agg_participants, np.mean, network_weight_updates, network_bias_updates) if clear_nans: aggregated_weights, aggregated_biases = nans_to_zero(aggregated_weights, aggregated_biases) # Apply update apply_updates(global_model, model_substitution_rate, aggregated_weights, aggregated_biases) # Robust aggregation via median elif discard_outliers == 'median': # Compute the aggregate as the component-wise median of local updates aggregated_weights, aggregated_biases = aggregate(n_layers, n_participants, np.median, network_weight_updates, network_bias_updates) if clear_nans: aggregated_weights, aggregated_biases = nans_to_zero(aggregated_weights, aggregated_biases) # Apply update apply_updates(global_model, model_substitution_rate, aggregated_weights, aggregated_biases) # KRUM elif discard_outliers == 'krum': # First, we build a distance matrix for parameters P = list(zip(network_weight_updates, network_bias_updates)) dist_matrix = [[dist_parameters(wi,bi,wj,bj) for wj,bj in P] for wi,bi in P] scores = [] for index in range(len(P)): distances_to_index = np.array(dist_matrix[index]) closest_to_index = np.argpartition(distances_to_index, n_participants-tolerance-1)[:n_participants-tolerance-1] scores.append(np.sum(distances_to_index[closest_to_index])) best = np.argmin(scores) aggregated_weights = network_weight_updates[best] aggregated_biases = network_bias_updates[best] if clear_nans: aggregated_weights, aggregated_biases = nans_to_zero(aggregated_weights, aggregated_biases) # Apply update apply_updates(global_model, model_substitution_rate, aggregated_weights, aggregated_biases) # Fallback case: no detection of outliers else: # Proceed as in first case aggregated_weights, aggregated_biases = aggregate(n_layers, n_participants, np.mean, network_weight_updates, network_bias_updates) if clear_nans: aggregated_weights, aggregated_biases = nans_to_zero(aggregated_weights, aggregated_biases) apply_updates(global_model, model_substitution_rate, aggregated_weights, aggregated_biases) ################### # COMPUTE METRICS # ################### # Global model accuracy score = global_model.evaluate(X_test, y_test, verbose=0) print(f'Global model loss: {score[0]}; global model accuracy: {score[1]}') metrics['accuracy'].append(score[1]) # Accuracy without the target score = global_model.evaluate(X_test, y_test, verbose=0) metrics['acc_no_target'].append(score[1]) # Proportion of instances of the target class misclassified (a.k.a success of the attack) #mat = confusion_matrix(np.argmax(shard1_traintest[i]["y_test"], axis=1), np.argmax(global_model.predict(shard1_traintest[i]["X_test"]), axis=1)) #trans_4_7 = (mat[4,7] - mat[4,4]) / (2 * (mat[4,4]+mat[4,7])) + 0.5 #metrics['atk_effectivity'].append(trans_4_7) # Distance of individual updates to the final aggregation metrics['update_distances'].append([dist_weights(aggregated_weights, w_i) for w_i in network_weight_updates]) savecsv(dic,"random_activity_100.csv")
Train on 18068 samples, validate on 136002 samples Epoch 1/100 18068/18068 [==============================] - ETA: 39s - loss: 0.6788 - accuracy: 0.562 - ETA: 0s - loss: 0.6006 - accuracy: 0.691 - ETA: 0s - loss: 0.5092 - accuracy: 0.76 - ETA: 0s - loss: 0.4488 - accuracy: 0.79 - ETA: 0s - loss: 0.4155 - accuracy: 0.81 - ETA: 0s - loss: 0.3891 - accuracy: 0.82 - ETA: 0s - loss: 0.3593 - accuracy: 0.84 - ETA: 0s - loss: 0.3467 - accuracy: 0.85 - 2s 105us/step - loss: 0.3436 - accuracy: 0.8538 - val_loss: 0.2597 - val_accuracy: 0.8811 Epoch 2/100 18068/18068 [==============================] - ETA: 0s - loss: 0.2291 - accuracy: 0.90 - ETA: 0s - loss: 0.2141 - accuracy: 0.91 - ETA: 0s - loss: 0.2275 - accuracy: 0.90 - ETA: 0s - loss: 0.2265 - accuracy: 0.90 - ETA: 0s - loss: 0.2190 - accuracy: 0.91 - ETA: 0s - loss: 0.2150 - accuracy: 0.91 - ETA: 0s - loss: 0.2104 - accuracy: 0.91 - ETA: 0s - loss: 0.2042 - accuracy: 0.91 - 2s 101us/step - loss: 0.2036 - accuracy: 0.9200 - val_loss: 0.1762 - val_accuracy: 0.9202 Epoch 3/100 18068/18068 [==============================] - ETA: 0s - loss: 0.1665 - accuracy: 0.93 - ETA: 0s - loss: 0.1756 - accuracy: 0.93 - ETA: 0s - loss: 0.1940 - accuracy: 0.92 - ETA: 0s - loss: 0.1856 - accuracy: 0.92 - ETA: 0s - loss: 0.1829 - accuracy: 0.92 - ETA: 0s - loss: 0.1831 - accuracy: 0.92 - ETA: 0s - loss: 0.1887 - accuracy: 0.92 - ETA: 0s - loss: 0.1854 - accuracy: 0.92 - 2s 102us/step - loss: 0.1833 - accuracy: 0.9264 - val_loss: 0.1747 - val_accuracy: 0.9309 Epoch 4/100 18068/18068 [==============================] - ETA: 0s - loss: 0.3563 - accuracy: 0.78 - ETA: 0s - loss: 0.1465 - accuracy: 0.94 - ETA: 0s - loss: 0.1444 - accuracy: 0.94 - ETA: 0s - loss: 0.1452 - accuracy: 0.94 - ETA: 0s - loss: 0.1484 - accuracy: 0.94 - ETA: 0s - loss: 0.1492 - accuracy: 0.94 - ETA: 0s - loss: 0.1500 - accuracy: 0.94 - ETA: 0s - loss: 0.1510 - accuracy: 0.94 - 2s 96us/step - loss: 0.1506 - accuracy: 0.9438 - val_loss: 0.1533 - val_accuracy: 0.9412 Epoch 5/100 18068/18068 [==============================] - ETA: 0s - loss: 0.1071 - accuracy: 0.93 - ETA: 0s - loss: 0.1414 - accuracy: 0.95 - ETA: 0s - loss: 0.1402 - accuracy: 0.95 - ETA: 0s - loss: 0.1363 - accuracy: 0.95 - ETA: 0s - loss: 0.1369 - accuracy: 0.95 - ETA: 0s - loss: 0.1350 - accuracy: 0.95 - ETA: 0s - loss: 0.1391 - accuracy: 0.94 - 2s 99us/step - loss: 0.1414 - accuracy: 0.9487 - val_loss: 0.1322 - val_accuracy: 0.9524 Epoch 6/100 18068/18068 [==============================] - ETA: 0s - loss: 0.1931 - accuracy: 0.90 - ETA: 0s - loss: 0.1383 - accuracy: 0.94 - ETA: 0s - loss: 0.1314 - accuracy: 0.95 - ETA: 0s - loss: 0.1356 - accuracy: 0.95 - ETA: 0s - loss: 0.1363 - accuracy: 0.95 - ETA: 0s - loss: 0.1372 - accuracy: 0.95 - ETA: 0s - loss: 0.1372 - accuracy: 0.95 - 2s 100us/step - loss: 0.1405 - accuracy: 0.9487 - val_loss: 0.1310 - val_accuracy: 0.9576 Epoch 7/100 18068/18068 [==============================] - ETA: 1s - loss: 0.0796 - accuracy: 1.00 - ETA: 0s - loss: 0.1566 - accuracy: 0.94 - ETA: 0s - loss: 0.1336 - accuracy: 0.95 - ETA: 0s - loss: 0.1296 - accuracy: 0.95 - ETA: 0s - loss: 0.1308 - accuracy: 0.95 - ETA: 0s - loss: 0.1323 - accuracy: 0.95 - ETA: 0s - loss: 0.1299 - accuracy: 0.95 - ETA: 0s - loss: 0.1291 - accuracy: 0.95 - 2s 100us/step - loss: 0.1291 - accuracy: 0.9533 - val_loss: 0.1146 - val_accuracy: 0.9584 Epoch 8/100 18068/18068 [==============================] - ETA: 1s - loss: 0.0266 - accuracy: 1.00 - ETA: 0s - loss: 0.1207 - accuracy: 0.96 - ETA: 0s - loss: 0.1266 - accuracy: 0.95 - ETA: 0s - loss: 0.1208 - accuracy: 0.96 - ETA: 0s - loss: 0.1163 - accuracy: 0.96 - ETA: 0s - loss: 0.1196 - accuracy: 0.95 - ETA: 0s - loss: 0.1160 - accuracy: 0.96 - ETA: 0s - loss: 0.1160 - accuracy: 0.96 - 2s 97us/step - loss: 0.1161 - accuracy: 0.9610 - val_loss: 0.1006 - val_accuracy: 0.9674 Epoch 9/100 18068/18068 [==============================] - ETA: 1s - loss: 0.0423 - accuracy: 1.00 - ETA: 0s - loss: 0.1142 - accuracy: 0.96 - ETA: 0s - loss: 0.1189 - accuracy: 0.95 - ETA: 0s - loss: 0.1210 - accuracy: 0.95 - ETA: 0s - loss: 0.1186 - accuracy: 0.95 - ETA: 0s - loss: 0.1148 - accuracy: 0.95 - ETA: 0s - loss: 0.1155 - accuracy: 0.95 - 2s 99us/step - loss: 0.1134 - accuracy: 0.9593 - val_loss: 0.1345 - val_accuracy: 0.9470 Epoch 10/100 18068/18068 [==============================] - ETA: 1s - loss: 0.1500 - accuracy: 0.93 - ETA: 0s - loss: 0.1266 - accuracy: 0.94 - ETA: 0s - loss: 0.1323 - accuracy: 0.94 - ETA: 0s - loss: 0.1268 - accuracy: 0.95 - ETA: 0s - loss: 0.1307 - accuracy: 0.95 - ETA: 0s - loss: 0.1280 - accuracy: 0.95 - ETA: 0s - loss: 0.1250 - accuracy: 0.95 - ETA: 0s - loss: 0.1211 - accuracy: 0.95 - 2s 100us/step - loss: 0.1208 - accuracy: 0.9557 - val_loss: 0.0943 - val_accuracy: 0.9679 Epoch 11/100 18068/18068 [==============================] - ETA: 0s - loss: 0.0425 - accuracy: 1.00 - ETA: 0s - loss: 0.1098 - accuracy: 0.95 - ETA: 0s - loss: 0.0987 - accuracy: 0.96 - ETA: 0s - loss: 0.1052 - accuracy: 0.96 - ETA: 0s - loss: 0.1071 - accuracy: 0.96 - ETA: 0s - loss: 0.1099 - accuracy: 0.95 - ETA: 0s - loss: 0.1124 - accuracy: 0.95 - 2s 100us/step - loss: 0.1155 - accuracy: 0.9574 - val_loss: 0.1408 - val_accuracy: 0.9464 Epoch 12/100 18068/18068 [==============================] - ETA: 0s - loss: 0.0991 - accuracy: 0.96 - ETA: 0s - loss: 0.1338 - accuracy: 0.95 - ETA: 0s - loss: 0.1271 - accuracy: 0.95 - ETA: 0s - loss: 0.1174 - accuracy: 0.95 - ETA: 0s - loss: 0.1149 - accuracy: 0.95 - ETA: 0s - loss: 0.1109 - accuracy: 0.96 - ETA: 0s - loss: 0.1107 - accuracy: 0.96 - ETA: 0s - loss: 0.1114 - accuracy: 0.96 - 2s 104us/step - loss: 0.1107 - accuracy: 0.9619 - val_loss: 0.0843 - val_accuracy: 0.9707 Epoch 13/100 18068/18068 [==============================] - ETA: 1s - loss: 0.1949 - accuracy: 0.90 - ETA: 0s - loss: 0.0911 - accuracy: 0.96 - ETA: 0s - loss: 0.0872 - accuracy: 0.96 - ETA: 0s - loss: 0.0943 - accuracy: 0.96 - ETA: 0s - loss: 0.1002 - accuracy: 0.96 - ETA: 0s - loss: 0.1068 - accuracy: 0.96 - ETA: 0s - loss: 0.1112 - accuracy: 0.95 - 2s 102us/step - loss: 0.1098 - accuracy: 0.9592 - val_loss: 0.0916 - val_accuracy: 0.9700 Epoch 14/100 18068/18068 [==============================] - ETA: 1s - loss: 0.0711 - accuracy: 0.96 - ETA: 0s - loss: 0.0996 - accuracy: 0.96 - ETA: 0s - loss: 0.1074 - accuracy: 0.96 - ETA: 0s - loss: 0.1204 - accuracy: 0.95 - ETA: 0s - loss: 0.1184 - accuracy: 0.95 - ETA: 0s - loss: 0.1118 - accuracy: 0.95 - ETA: 0s - loss: 0.1081 - accuracy: 0.96 - 2s 97us/step - loss: 0.1091 - accuracy: 0.9604 - val_loss: 0.1071 - val_accuracy: 0.9602 Epoch 15/100 18068/18068 [==============================] - ETA: 0s - loss: 0.0172 - accuracy: 1.00 - ETA: 0s - loss: 0.1060 - accuracy: 0.96 - ETA: 0s - loss: 0.1036 - accuracy: 0.95 - ETA: 0s - loss: 0.1007 - accuracy: 0.96 - ETA: 0s - loss: 0.0968 - accuracy: 0.96 - ETA: 0s - loss: 0.0999 - accuracy: 0.96 - ETA: 0s - loss: 0.1008 - accuracy: 0.96 - 2s 98us/step - loss: 0.0995 - accuracy: 0.9649 - val_loss: 0.1024 - val_accuracy: 0.9661 Epoch 16/100 18068/18068 [==============================] - ETA: 0s - loss: 0.0327 - accuracy: 1.00 - ETA: 0s - loss: 0.0968 - accuracy: 0.96 - ETA: 0s - loss: 0.1199 - accuracy: 0.95 - ETA: 0s - loss: 0.1218 - accuracy: 0.95 - ETA: 0s - loss: 0.1172 - accuracy: 0.95 - ETA: 0s - loss: 0.1132 - accuracy: 0.95 - ETA: 0s - loss: 0.1132 - accuracy: 0.96 - 2s 99us/step - loss: 0.1109 - accuracy: 0.9613 - val_loss: 0.0845 - val_accuracy: 0.9723 Epoch 17/100 18068/18068 [==============================] - ETA: 0s - loss: 0.0223 - accuracy: 1.00 - ETA: 0s - loss: 0.0942 - accuracy: 0.96 - ETA: 0s - loss: 0.0892 - accuracy: 0.96 - ETA: 0s - loss: 0.0889 - accuracy: 0.96 - ETA: 0s - loss: 0.0900 - accuracy: 0.96 - ETA: 0s - loss: 0.0925 - accuracy: 0.96 - ETA: 0s - loss: 0.0982 - accuracy: 0.96 - ETA: 0s - loss: 0.0954 - accuracy: 0.96 - 2s 99us/step - loss: 0.0955 - accuracy: 0.9666 - val_loss: 0.0854 - val_accuracy: 0.9719 Epoch 18/100
18068/18068 [==============================] - ETA: 0s - loss: 0.0278 - accuracy: 1.00 - ETA: 0s - loss: 0.0835 - accuracy: 0.97 - ETA: 0s - loss: 0.0847 - accuracy: 0.97 - ETA: 0s - loss: 0.0903 - accuracy: 0.96 - ETA: 0s - loss: 0.0930 - accuracy: 0.96 - ETA: 0s - loss: 0.0942 - accuracy: 0.96 - ETA: 0s - loss: 0.0909 - accuracy: 0.96 - 2s 100us/step - loss: 0.0904 - accuracy: 0.9677 - val_loss: 0.1059 - val_accuracy: 0.9670 Epoch 19/100 18068/18068 [==============================] - ETA: 0s - loss: 0.1972 - accuracy: 0.93 - ETA: 0s - loss: 0.0773 - accuracy: 0.97 - ETA: 0s - loss: 0.0849 - accuracy: 0.97 - ETA: 0s - loss: 0.0869 - accuracy: 0.97 - ETA: 0s - loss: 0.0817 - accuracy: 0.97 - ETA: 0s - loss: 0.0803 - accuracy: 0.97 - ETA: 0s - loss: 0.0845 - accuracy: 0.97 - 2s 97us/step - loss: 0.0852 - accuracy: 0.9716 - val_loss: 0.1073 - val_accuracy: 0.9576 Epoch 20/100 18068/18068 [==============================] - ETA: 0s - loss: 0.1471 - accuracy: 0.93 - ETA: 0s - loss: 0.0899 - accuracy: 0.96 - ETA: 0s - loss: 0.0802 - accuracy: 0.97 - ETA: 0s - loss: 0.0826 - accuracy: 0.97 - ETA: 0s - loss: 0.0898 - accuracy: 0.96 - ETA: 0s - loss: 0.0912 - accuracy: 0.96 - ETA: 0s - loss: 0.0898 - accuracy: 0.96 - 2s 99us/step - loss: 0.0899 - accuracy: 0.9690 - val_loss: 0.0760 - val_accuracy: 0.9753 Epoch 21/100 18068/18068 [==============================] - ETA: 0s - loss: 0.1002 - accuracy: 0.96 - ETA: 0s - loss: 0.0704 - accuracy: 0.97 - ETA: 0s - loss: 0.0741 - accuracy: 0.97 - ETA: 0s - loss: 0.0735 - accuracy: 0.97 - ETA: 0s - loss: 0.0729 - accuracy: 0.97 - ETA: 0s - loss: 0.0734 - accuracy: 0.97 - ETA: 0s - loss: 0.0755 - accuracy: 0.97 - 2s 102us/step - loss: 0.0755 - accuracy: 0.9735 - val_loss: 0.0788 - val_accuracy: 0.9719 Epoch 22/100 18068/18068 [==============================] - ETA: 0s - loss: 0.2126 - accuracy: 0.90 - ETA: 0s - loss: 0.0985 - accuracy: 0.96 - ETA: 0s - loss: 0.0866 - accuracy: 0.96 - ETA: 0s - loss: 0.0771 - accuracy: 0.97 - ETA: 0s - loss: 0.0784 - accuracy: 0.97 - ETA: 0s - loss: 0.0791 - accuracy: 0.97 - ETA: 0s - loss: 0.0772 - accuracy: 0.97 - 2s 97us/step - loss: 0.0767 - accuracy: 0.9727 - val_loss: 0.0837 - val_accuracy: 0.9757 Epoch 23/100 18068/18068 [==============================] - ETA: 0s - loss: 0.0565 - accuracy: 0.96 - ETA: 0s - loss: 0.0798 - accuracy: 0.97 - ETA: 0s - loss: 0.0660 - accuracy: 0.97 - ETA: 0s - loss: 0.0723 - accuracy: 0.97 - ETA: 0s - loss: 0.0793 - accuracy: 0.97 - ETA: 0s - loss: 0.0781 - accuracy: 0.97 - ETA: 0s - loss: 0.0759 - accuracy: 0.97 - 2s 98us/step - loss: 0.0767 - accuracy: 0.9733 - val_loss: 0.0879 - val_accuracy: 0.9695 Epoch 24/100 18068/18068 [==============================] - ETA: 0s - loss: 0.0170 - accuracy: 1.00 - ETA: 0s - loss: 0.0718 - accuracy: 0.97 - ETA: 0s - loss: 0.0800 - accuracy: 0.97 - ETA: 0s - loss: 0.0783 - accuracy: 0.97 - ETA: 0s - loss: 0.0785 - accuracy: 0.97 - ETA: 0s - loss: 0.0784 - accuracy: 0.97 - ETA: 0s - loss: 0.0799 - accuracy: 0.97 - 2s 98us/step - loss: 0.0807 - accuracy: 0.9716 - val_loss: 0.0583 - val_accuracy: 0.9794 Epoch 25/100 18068/18068 [==============================] - ETA: 1s - loss: 0.0030 - accuracy: 1.00 - ETA: 0s - loss: 0.0636 - accuracy: 0.97 - ETA: 0s - loss: 0.0630 - accuracy: 0.97 - ETA: 0s - loss: 0.0804 - accuracy: 0.97 - ETA: 0s - loss: 0.0770 - accuracy: 0.97 - ETA: 0s - loss: 0.0748 - accuracy: 0.97 - ETA: 0s - loss: 0.0725 - accuracy: 0.97 - ETA: 0s - loss: 0.0742 - accuracy: 0.97 - 2s 97us/step - loss: 0.0745 - accuracy: 0.9740 - val_loss: 0.0661 - val_accuracy: 0.9816 Epoch 26/100 18068/18068 [==============================] - ETA: 0s - loss: 0.0948 - accuracy: 0.93 - ETA: 0s - loss: 0.0584 - accuracy: 0.97 - ETA: 0s - loss: 0.0578 - accuracy: 0.97 - ETA: 0s - loss: 0.0676 - accuracy: 0.97 - ETA: 0s - loss: 0.0804 - accuracy: 0.96 - ETA: 0s - loss: 0.0886 - accuracy: 0.96 - ETA: 0s - loss: 0.0872 - accuracy: 0.96 - 2s 98us/step - loss: 0.0849 - accuracy: 0.9677 - val_loss: 0.0790 - val_accuracy: 0.9766 Epoch 27/100 18068/18068 [==============================] - ETA: 0s - loss: 0.0358 - accuracy: 1.00 - ETA: 0s - loss: 0.0517 - accuracy: 0.98 - ETA: 0s - loss: 0.0588 - accuracy: 0.98 - ETA: 0s - loss: 0.0719 - accuracy: 0.97 - ETA: 0s - loss: 0.0696 - accuracy: 0.97 - ETA: 0s - loss: 0.0659 - accuracy: 0.97 - ETA: 0s - loss: 0.0649 - accuracy: 0.97 - 2s 99us/step - loss: 0.0643 - accuracy: 0.9778 - val_loss: 0.0584 - val_accuracy: 0.9804 Epoch 28/100 18068/18068 [==============================] - ETA: 1s - loss: 0.1423 - accuracy: 0.93 - ETA: 0s - loss: 0.0831 - accuracy: 0.96 - ETA: 0s - loss: 0.0703 - accuracy: 0.97 - ETA: 0s - loss: 0.0680 - accuracy: 0.97 - ETA: 0s - loss: 0.0644 - accuracy: 0.97 - ETA: 0s - loss: 0.0650 - accuracy: 0.97 - ETA: 0s - loss: 0.0664 - accuracy: 0.97 - 2s 100us/step - loss: 0.0649 - accuracy: 0.9760 - val_loss: 0.0610 - val_accuracy: 0.9821 Epoch 29/100 18068/18068 [==============================] - ETA: 0s - loss: 0.0349 - accuracy: 0.96 - ETA: 0s - loss: 0.0645 - accuracy: 0.97 - ETA: 0s - loss: 0.0565 - accuracy: 0.98 - ETA: 0s - loss: 0.0675 - accuracy: 0.97 - ETA: 0s - loss: 0.0673 - accuracy: 0.97 - ETA: 0s - loss: 0.0667 - accuracy: 0.97 - ETA: 0s - loss: 0.0663 - accuracy: 0.97 - 2s 97us/step - loss: 0.0650 - accuracy: 0.9775 - val_loss: 0.0835 - val_accuracy: 0.9671 Epoch 30/100 18068/18068 [==============================] - ETA: 0s - loss: 0.0869 - accuracy: 0.96 - ETA: 0s - loss: 0.0555 - accuracy: 0.98 - ETA: 0s - loss: 0.0522 - accuracy: 0.98 - ETA: 0s - loss: 0.0543 - accuracy: 0.98 - ETA: 0s - loss: 0.0567 - accuracy: 0.98 - ETA: 0s - loss: 0.0611 - accuracy: 0.97 - ETA: 0s - loss: 0.0642 - accuracy: 0.97 - 2s 100us/step - loss: 0.0638 - accuracy: 0.9774 - val_loss: 0.0530 - val_accuracy: 0.9830 Epoch 31/100 18068/18068 [==============================] - ETA: 1s - loss: 0.0032 - accuracy: 1.00 - ETA: 0s - loss: 0.0556 - accuracy: 0.98 - ETA: 0s - loss: 0.0615 - accuracy: 0.97 - ETA: 0s - loss: 0.0574 - accuracy: 0.98 - ETA: 0s - loss: 0.0574 - accuracy: 0.98 - ETA: 0s - loss: 0.0583 - accuracy: 0.98 - ETA: 0s - loss: 0.0581 - accuracy: 0.98 - 2s 95us/step - loss: 0.0563 - accuracy: 0.9813 - val_loss: 0.0531 - val_accuracy: 0.9849 Epoch 32/100 18068/18068 [==============================] - ETA: 0s - loss: 0.0303 - accuracy: 1.00 - ETA: 0s - loss: 0.0350 - accuracy: 0.98 - ETA: 0s - loss: 0.0485 - accuracy: 0.98 - ETA: 0s - loss: 0.0524 - accuracy: 0.98 - ETA: 0s - loss: 0.0515 - accuracy: 0.98 - ETA: 0s - loss: 0.0588 - accuracy: 0.97 - ETA: 0s - loss: 0.0606 - accuracy: 0.97 - 2s 99us/step - loss: 0.0622 - accuracy: 0.9787 - val_loss: 0.0634 - val_accuracy: 0.9815 Epoch 33/100 18068/18068 [==============================] - ETA: 1s - loss: 0.0507 - accuracy: 1.00 - ETA: 0s - loss: 0.0457 - accuracy: 0.98 - ETA: 0s - loss: 0.0464 - accuracy: 0.98 - ETA: 0s - loss: 0.0504 - accuracy: 0.98 - ETA: 0s - loss: 0.0511 - accuracy: 0.98 - ETA: 0s - loss: 0.0512 - accuracy: 0.98 - ETA: 0s - loss: 0.0510 - accuracy: 0.98 - 2s 101us/step - loss: 0.0508 - accuracy: 0.9833 - val_loss: 0.0499 - val_accuracy: 0.9864 Epoch 34/100 18068/18068 [==============================] - ETA: 0s - loss: 0.0097 - accuracy: 1.00 - ETA: 0s - loss: 0.0488 - accuracy: 0.98 - ETA: 0s - loss: 0.0614 - accuracy: 0.98 - ETA: 0s - loss: 0.0627 - accuracy: 0.98 - ETA: 0s - loss: 0.0614 - accuracy: 0.98 - ETA: 0s - loss: 0.0615 - accuracy: 0.98 - ETA: 0s - loss: 0.0592 - accuracy: 0.98 - 2s 93us/step - loss: 0.0572 - accuracy: 0.9815 - val_loss: 0.0420 - val_accuracy: 0.9871 Epoch 35/100 18068/18068 [==============================] - ETA: 0s - loss: 0.0795 - accuracy: 0.96 - ETA: 0s - loss: 0.0474 - accuracy: 0.98 - ETA: 0s - loss: 0.0459 - accuracy: 0.98 - ETA: 0s - loss: 0.0480 - accuracy: 0.98 - ETA: 0s - loss: 0.0458 - accuracy: 0.98 - ETA: 0s - loss: 0.0477 - accuracy: 0.98 - ETA: 0s - loss: 0.0560 - accuracy: 0.98 - 2s 95us/step - loss: 0.0579 - accuracy: 0.9807 - val_loss: 0.0606 - val_accuracy: 0.9777 Epoch 36/100
18068/18068 [==============================] - ETA: 0s - loss: 0.0130 - accuracy: 1.00 - ETA: 0s - loss: 0.0689 - accuracy: 0.97 - ETA: 0s - loss: 0.0682 - accuracy: 0.97 - ETA: 0s - loss: 0.0658 - accuracy: 0.97 - ETA: 0s - loss: 0.0601 - accuracy: 0.97 - ETA: 0s - loss: 0.0572 - accuracy: 0.98 - ETA: 0s - loss: 0.0540 - accuracy: 0.98 - 2s 93us/step - loss: 0.0523 - accuracy: 0.9823 - val_loss: 0.0444 - val_accuracy: 0.9877 Epoch 37/100 18068/18068 [==============================] - ETA: 0s - loss: 0.0141 - accuracy: 1.00 - ETA: 0s - loss: 0.0639 - accuracy: 0.97 - ETA: 0s - loss: 0.0546 - accuracy: 0.97 - ETA: 0s - loss: 0.0501 - accuracy: 0.98 - ETA: 0s - loss: 0.0509 - accuracy: 0.98 - ETA: 0s - loss: 0.0528 - accuracy: 0.98 - ETA: 0s - loss: 0.0533 - accuracy: 0.98 - 2s 95us/step - loss: 0.0527 - accuracy: 0.9806 - val_loss: 0.0399 - val_accuracy: 0.9892 Epoch 38/100 18068/18068 [==============================] - ETA: 0s - loss: 0.0120 - accuracy: 1.00 - ETA: 0s - loss: 0.0439 - accuracy: 0.98 - ETA: 0s - loss: 0.0460 - accuracy: 0.98 - ETA: 0s - loss: 0.0474 - accuracy: 0.98 - ETA: 0s - loss: 0.0488 - accuracy: 0.98 - ETA: 0s - loss: 0.0470 - accuracy: 0.98 - ETA: 0s - loss: 0.0468 - accuracy: 0.98 - 2s 97us/step - loss: 0.0490 - accuracy: 0.9841 - val_loss: 0.0560 - val_accuracy: 0.9819 Epoch 39/100 18068/18068 [==============================] - ETA: 0s - loss: 0.0037 - accuracy: 1.00 - ETA: 0s - loss: 0.0482 - accuracy: 0.98 - ETA: 0s - loss: 0.0398 - accuracy: 0.98 - ETA: 0s - loss: 0.0396 - accuracy: 0.98 - ETA: 0s - loss: 0.0402 - accuracy: 0.98 - ETA: 0s - loss: 0.0419 - accuracy: 0.98 - ETA: 0s - loss: 0.0441 - accuracy: 0.98 - 2s 98us/step - loss: 0.0457 - accuracy: 0.9843 - val_loss: 0.0492 - val_accuracy: 0.9844 Epoch 40/100 18068/18068 [==============================] - ETA: 0s - loss: 0.0412 - accuracy: 0.96 - ETA: 0s - loss: 0.0411 - accuracy: 0.98 - ETA: 0s - loss: 0.0500 - accuracy: 0.98 - ETA: 0s - loss: 0.0472 - accuracy: 0.98 - ETA: 0s - loss: 0.0477 - accuracy: 0.98 - ETA: 0s - loss: 0.0470 - accuracy: 0.98 - ETA: 0s - loss: 0.0469 - accuracy: 0.98 - 2s 97us/step - loss: 0.0465 - accuracy: 0.9842 - val_loss: 0.0679 - val_accuracy: 0.9757 Epoch 41/100 18068/18068 [==============================] - ETA: 0s - loss: 0.0218 - accuracy: 1.00 - ETA: 0s - loss: 0.0453 - accuracy: 0.98 - ETA: 0s - loss: 0.0404 - accuracy: 0.98 - ETA: 0s - loss: 0.0391 - accuracy: 0.98 - ETA: 0s - loss: 0.0435 - accuracy: 0.98 - ETA: 0s - loss: 0.0439 - accuracy: 0.98 - ETA: 0s - loss: 0.0423 - accuracy: 0.98 - 2s 95us/step - loss: 0.0461 - accuracy: 0.9846 - val_loss: 0.0624 - val_accuracy: 0.9818 Epoch 42/100 18068/18068 [==============================] - ETA: 1s - loss: 0.1395 - accuracy: 0.96 - ETA: 0s - loss: 0.0579 - accuracy: 0.97 - ETA: 0s - loss: 0.0520 - accuracy: 0.98 - ETA: 0s - loss: 0.0567 - accuracy: 0.97 - ETA: 0s - loss: 0.0531 - accuracy: 0.98 - ETA: 0s - loss: 0.0481 - accuracy: 0.98 - ETA: 0s - loss: 0.0461 - accuracy: 0.98 - 2s 97us/step - loss: 0.0461 - accuracy: 0.9842 - val_loss: 0.0568 - val_accuracy: 0.9820 Epoch 43/100 18068/18068 [==============================] - ETA: 0s - loss: 0.1796 - accuracy: 0.93 - ETA: 0s - loss: 0.0395 - accuracy: 0.98 - ETA: 0s - loss: 0.0388 - accuracy: 0.98 - ETA: 0s - loss: 0.0365 - accuracy: 0.98 - ETA: 0s - loss: 0.0386 - accuracy: 0.98 - ETA: 0s - loss: 0.0428 - accuracy: 0.98 - ETA: 0s - loss: 0.0423 - accuracy: 0.98 - ETA: 0s - loss: 0.0420 - accuracy: 0.98 - 2s 95us/step - loss: 0.0415 - accuracy: 0.9852 - val_loss: 0.0502 - val_accuracy: 0.9832 Epoch 44/100 18068/18068 [==============================] - ETA: 0s - loss: 0.0091 - accuracy: 1.00 - ETA: 0s - loss: 0.0421 - accuracy: 0.98 - ETA: 0s - loss: 0.0521 - accuracy: 0.98 - ETA: 0s - loss: 0.0472 - accuracy: 0.98 - ETA: 0s - loss: 0.0458 - accuracy: 0.98 - ETA: 0s - loss: 0.0460 - accuracy: 0.98 - ETA: 0s - loss: 0.0442 - accuracy: 0.98 - 2s 97us/step - loss: 0.0442 - accuracy: 0.9846 - val_loss: 0.0770 - val_accuracy: 0.9756 Epoch 45/100 18068/18068 [==============================] - ETA: 0s - loss: 0.1832 - accuracy: 0.90 - ETA: 0s - loss: 0.0467 - accuracy: 0.98 - ETA: 0s - loss: 0.0432 - accuracy: 0.98 - ETA: 0s - loss: 0.0455 - accuracy: 0.98 - ETA: 0s - loss: 0.0492 - accuracy: 0.98 - ETA: 0s - loss: 0.0501 - accuracy: 0.98 - ETA: 0s - loss: 0.0489 - accuracy: 0.98 - 2s 95us/step - loss: 0.0478 - accuracy: 0.9835 - val_loss: 0.0404 - val_accuracy: 0.9876 Epoch 46/100 18068/18068 [==============================] - ETA: 0s - loss: 0.1283 - accuracy: 0.93 - ETA: 0s - loss: 0.0580 - accuracy: 0.98 - ETA: 0s - loss: 0.0460 - accuracy: 0.98 - ETA: 0s - loss: 0.0409 - accuracy: 0.98 - ETA: 0s - loss: 0.0432 - accuracy: 0.98 - ETA: 0s - loss: 0.0453 - accuracy: 0.98 - ETA: 0s - loss: 0.0464 - accuracy: 0.98 - 2s 97us/step - loss: 0.0454 - accuracy: 0.9852 - val_loss: 0.0507 - val_accuracy: 0.9834 Epoch 47/100 18068/18068 [==============================] - ETA: 0s - loss: 0.0083 - accuracy: 1.00 - ETA: 0s - loss: 0.0326 - accuracy: 0.99 - ETA: 0s - loss: 0.0341 - accuracy: 0.98 - ETA: 0s - loss: 0.0374 - accuracy: 0.98 - ETA: 0s - loss: 0.0397 - accuracy: 0.98 - ETA: 0s - loss: 0.0426 - accuracy: 0.98 - ETA: 0s - loss: 0.0426 - accuracy: 0.98 - 2s 95us/step - loss: 0.0431 - accuracy: 0.9860 - val_loss: 0.0376 - val_accuracy: 0.9880 Epoch 48/100 18068/18068 [==============================] - ETA: 1s - loss: 0.0127 - accuracy: 1.00 - ETA: 0s - loss: 0.0370 - accuracy: 0.98 - ETA: 0s - loss: 0.0380 - accuracy: 0.98 - ETA: 0s - loss: 0.0364 - accuracy: 0.98 - ETA: 0s - loss: 0.0375 - accuracy: 0.98 - ETA: 0s - loss: 0.0429 - accuracy: 0.98 - ETA: 0s - loss: 0.0431 - accuracy: 0.98 - 2s 93us/step - loss: 0.0416 - accuracy: 0.9862 - val_loss: 0.0383 - val_accuracy: 0.9888 Epoch 49/100 18068/18068 [==============================] - ETA: 0s - loss: 0.0018 - accuracy: 1.00 - ETA: 0s - loss: 0.0304 - accuracy: 0.98 - ETA: 0s - loss: 0.0320 - accuracy: 0.98 - ETA: 0s - loss: 0.0301 - accuracy: 0.98 - ETA: 0s - loss: 0.0392 - accuracy: 0.98 - ETA: 0s - loss: 0.0393 - accuracy: 0.98 - ETA: 0s - loss: 0.0391 - accuracy: 0.98 - 2s 97us/step - loss: 0.0398 - accuracy: 0.9867 - val_loss: 0.0483 - val_accuracy: 0.9870 Epoch 50/100 18068/18068 [==============================] - ETA: 1s - loss: 0.0272 - accuracy: 1.00 - ETA: 0s - loss: 0.0242 - accuracy: 0.99 - ETA: 0s - loss: 0.0285 - accuracy: 0.99 - ETA: 0s - loss: 0.0330 - accuracy: 0.98 - ETA: 0s - loss: 0.0439 - accuracy: 0.98 - ETA: 0s - loss: 0.0465 - accuracy: 0.98 - ETA: 0s - loss: 0.0441 - accuracy: 0.98 - 2s 96us/step - loss: 0.0455 - accuracy: 0.9846 - val_loss: 0.1553 - val_accuracy: 0.9496 Epoch 51/100 18068/18068 [==============================] - ETA: 0s - loss: 0.2229 - accuracy: 0.96 - ETA: 0s - loss: 0.0702 - accuracy: 0.97 - ETA: 0s - loss: 0.0525 - accuracy: 0.98 - ETA: 0s - loss: 0.0484 - accuracy: 0.98 - ETA: 0s - loss: 0.0464 - accuracy: 0.98 - ETA: 0s - loss: 0.0452 - accuracy: 0.98 - ETA: 0s - loss: 0.0438 - accuracy: 0.98 - 2s 98us/step - loss: 0.0427 - accuracy: 0.9851 - val_loss: 0.0405 - val_accuracy: 0.9872 Epoch 52/100 18068/18068 [==============================] - ETA: 0s - loss: 0.0044 - accuracy: 1.00 - ETA: 0s - loss: 0.0345 - accuracy: 0.98 - ETA: 0s - loss: 0.0323 - accuracy: 0.98 - ETA: 0s - loss: 0.0305 - accuracy: 0.98 - ETA: 0s - loss: 0.0303 - accuracy: 0.98 - ETA: 0s - loss: 0.0313 - accuracy: 0.98 - ETA: 0s - loss: 0.0353 - accuracy: 0.98 - 2s 101us/step - loss: 0.0372 - accuracy: 0.9867 - val_loss: 0.0507 - val_accuracy: 0.9849 Epoch 53/100 18068/18068 [==============================] - ETA: 0s - loss: 0.0992 - accuracy: 0.96 - ETA: 0s - loss: 0.0372 - accuracy: 0.98 - ETA: 0s - loss: 0.0325 - accuracy: 0.98 - ETA: 0s - loss: 0.0338 - accuracy: 0.98 - ETA: 0s - loss: 0.0350 - accuracy: 0.98 - ETA: 0s - loss: 0.0336 - accuracy: 0.98 - ETA: 0s - loss: 0.0330 - accuracy: 0.98 - 2s 96us/step - loss: 0.0341 - accuracy: 0.9890 - val_loss: 0.0706 - val_accuracy: 0.9775 Epoch 54/100
18068/18068 [==============================] - ETA: 0s - loss: 0.0255 - accuracy: 1.00 - ETA: 0s - loss: 0.0471 - accuracy: 0.98 - ETA: 0s - loss: 0.0442 - accuracy: 0.98 - ETA: 0s - loss: 0.0541 - accuracy: 0.98 - ETA: 0s - loss: 0.0506 - accuracy: 0.98 - ETA: 0s - loss: 0.0458 - accuracy: 0.98 - ETA: 0s - loss: 0.0431 - accuracy: 0.98 - 2s 98us/step - loss: 0.0431 - accuracy: 0.9851 - val_loss: 0.0332 - val_accuracy: 0.9903 Epoch 55/100 18068/18068 [==============================] - ETA: 0s - loss: 0.0017 - accuracy: 1.00 - ETA: 0s - loss: 0.0230 - accuracy: 0.99 - ETA: 0s - loss: 0.0270 - accuracy: 0.99 - ETA: 0s - loss: 0.0321 - accuracy: 0.98 - ETA: 0s - loss: 0.0424 - accuracy: 0.98 - ETA: 0s - loss: 0.0421 - accuracy: 0.98 - ETA: 0s - loss: 0.0391 - accuracy: 0.98 - 2s 98us/step - loss: 0.0380 - accuracy: 0.9869 - val_loss: 0.0261 - val_accuracy: 0.9926 Epoch 56/100 18068/18068 [==============================] - ETA: 1s - loss: 0.0202 - accuracy: 1.00 - ETA: 0s - loss: 0.0348 - accuracy: 0.98 - ETA: 0s - loss: 0.0271 - accuracy: 0.99 - ETA: 0s - loss: 0.0280 - accuracy: 0.99 - ETA: 0s - loss: 0.0316 - accuracy: 0.99 - ETA: 0s - loss: 0.0324 - accuracy: 0.98 - ETA: 0s - loss: 0.0369 - accuracy: 0.98 - 2s 100us/step - loss: 0.0365 - accuracy: 0.9878 - val_loss: 0.0421 - val_accuracy: 0.9857 Epoch 57/100 18068/18068 [==============================] - ETA: 0s - loss: 0.0309 - accuracy: 1.00 - ETA: 0s - loss: 0.0217 - accuracy: 0.99 - ETA: 0s - loss: 0.0268 - accuracy: 0.99 - ETA: 0s - loss: 0.0281 - accuracy: 0.99 - ETA: 0s - loss: 0.0261 - accuracy: 0.99 - ETA: 0s - loss: 0.0272 - accuracy: 0.99 - ETA: 0s - loss: 0.0348 - accuracy: 0.98 - 2s 100us/step - loss: 0.0378 - accuracy: 0.9872 - val_loss: 0.0563 - val_accuracy: 0.9826 Epoch 58/100 18068/18068 [==============================] - ETA: 0s - loss: 0.0666 - accuracy: 0.96 - ETA: 0s - loss: 0.0312 - accuracy: 0.99 - ETA: 0s - loss: 0.0239 - accuracy: 0.99 - ETA: 0s - loss: 0.0267 - accuracy: 0.99 - ETA: 0s - loss: 0.0328 - accuracy: 0.98 - ETA: 0s - loss: 0.0355 - accuracy: 0.98 - ETA: 0s - loss: 0.0363 - accuracy: 0.98 - 2s 100us/step - loss: 0.0372 - accuracy: 0.9870 - val_loss: 0.1505 - val_accuracy: 0.9517 Epoch 59/100 18068/18068 [==============================] - ETA: 0s - loss: 0.1813 - accuracy: 0.96 - ETA: 0s - loss: 0.0483 - accuracy: 0.98 - ETA: 0s - loss: 0.0370 - accuracy: 0.98 - ETA: 0s - loss: 0.0355 - accuracy: 0.98 - ETA: 0s - loss: 0.0349 - accuracy: 0.98 - ETA: 0s - loss: 0.0339 - accuracy: 0.98 - ETA: 0s - loss: 0.0337 - accuracy: 0.98 - 2s 102us/step - loss: 0.0329 - accuracy: 0.9886 - val_loss: 0.0502 - val_accuracy: 0.9844 Epoch 60/100 18068/18068 [==============================] - ETA: 1s - loss: 0.0191 - accuracy: 1.00 - ETA: 0s - loss: 0.0256 - accuracy: 0.99 - ETA: 0s - loss: 0.0333 - accuracy: 0.98 - ETA: 0s - loss: 0.0374 - accuracy: 0.98 - ETA: 0s - loss: 0.0342 - accuracy: 0.98 - ETA: 0s - loss: 0.0349 - accuracy: 0.98 - ETA: 0s - loss: 0.0343 - accuracy: 0.98 - 2s 97us/step - loss: 0.0381 - accuracy: 0.9862 - val_loss: 0.0817 - val_accuracy: 0.9748 Epoch 61/100 18068/18068 [==============================] - ETA: 0s - loss: 0.1110 - accuracy: 0.96 - ETA: 0s - loss: 0.0271 - accuracy: 0.99 - ETA: 0s - loss: 0.0375 - accuracy: 0.98 - ETA: 0s - loss: 0.0336 - accuracy: 0.98 - ETA: 0s - loss: 0.0310 - accuracy: 0.98 - ETA: 0s - loss: 0.0304 - accuracy: 0.98 - ETA: 0s - loss: 0.0320 - accuracy: 0.98 - 2s 94us/step - loss: 0.0326 - accuracy: 0.9883 - val_loss: 0.0324 - val_accuracy: 0.9908 Epoch 62/100 18068/18068 [==============================] - ETA: 1s - loss: 0.0520 - accuracy: 0.96 - ETA: 0s - loss: 0.0428 - accuracy: 0.98 - ETA: 0s - loss: 0.0428 - accuracy: 0.98 - ETA: 0s - loss: 0.0391 - accuracy: 0.98 - ETA: 0s - loss: 0.0362 - accuracy: 0.98 - ETA: 0s - loss: 0.0350 - accuracy: 0.98 - ETA: 0s - loss: 0.0353 - accuracy: 0.98 - 2s 94us/step - loss: 0.0344 - accuracy: 0.9882 - val_loss: 0.0375 - val_accuracy: 0.9880 Epoch 63/100 18068/18068 [==============================] - ETA: 0s - loss: 0.0866 - accuracy: 0.96 - ETA: 0s - loss: 0.0334 - accuracy: 0.98 - ETA: 0s - loss: 0.0292 - accuracy: 0.98 - ETA: 0s - loss: 0.0268 - accuracy: 0.99 - ETA: 0s - loss: 0.0276 - accuracy: 0.99 - ETA: 0s - loss: 0.0418 - accuracy: 0.98 - ETA: 0s - loss: 0.0397 - accuracy: 0.98 - 2s 97us/step - loss: 0.0384 - accuracy: 0.9880 - val_loss: 0.0308 - val_accuracy: 0.9910 Epoch 64/100 18068/18068 [==============================] - ETA: 1s - loss: 0.0635 - accuracy: 0.96 - ETA: 0s - loss: 0.0280 - accuracy: 0.99 - ETA: 0s - loss: 0.0262 - accuracy: 0.99 - ETA: 0s - loss: 0.0331 - accuracy: 0.98 - ETA: 0s - loss: 0.0329 - accuracy: 0.98 - ETA: 0s - loss: 0.0336 - accuracy: 0.98 - ETA: 0s - loss: 0.0309 - accuracy: 0.99 - 2s 98us/step - loss: 0.0302 - accuracy: 0.9904 - val_loss: 0.0318 - val_accuracy: 0.9913 Epoch 65/100 18068/18068 [==============================] - ETA: 1s - loss: 0.0202 - accuracy: 1.00 - ETA: 0s - loss: 0.0188 - accuracy: 0.99 - ETA: 0s - loss: 0.0229 - accuracy: 0.99 - ETA: 0s - loss: 0.0271 - accuracy: 0.99 - ETA: 0s - loss: 0.0267 - accuracy: 0.99 - ETA: 0s - loss: 0.0278 - accuracy: 0.99 - ETA: 0s - loss: 0.0303 - accuracy: 0.99 - 2s 98us/step - loss: 0.0297 - accuracy: 0.9904 - val_loss: 0.0412 - val_accuracy: 0.9882 Epoch 66/100 18068/18068 [==============================] - ETA: 0s - loss: 0.0062 - accuracy: 1.00 - ETA: 0s - loss: 0.0258 - accuracy: 0.99 - ETA: 0s - loss: 0.0294 - accuracy: 0.98 - ETA: 0s - loss: 0.0310 - accuracy: 0.98 - ETA: 0s - loss: 0.0311 - accuracy: 0.99 - ETA: 0s - loss: 0.0308 - accuracy: 0.98 - ETA: 0s - loss: 0.0336 - accuracy: 0.98 - 2s 99us/step - loss: 0.0329 - accuracy: 0.9893 - val_loss: 0.0662 - val_accuracy: 0.9754 Epoch 67/100 18068/18068 [==============================] - ETA: 1s - loss: 0.0316 - accuracy: 0.96 - ETA: 0s - loss: 0.0315 - accuracy: 0.98 - ETA: 0s - loss: 0.0266 - accuracy: 0.99 - ETA: 0s - loss: 0.0231 - accuracy: 0.99 - ETA: 0s - loss: 0.0263 - accuracy: 0.99 - ETA: 0s - loss: 0.0264 - accuracy: 0.99 - ETA: 0s - loss: 0.0271 - accuracy: 0.99 - 2s 95us/step - loss: 0.0269 - accuracy: 0.9915 - val_loss: 0.0258 - val_accuracy: 0.9924 Epoch 68/100 18068/18068 [==============================] - ETA: 1s - loss: 0.0184 - accuracy: 1.00 - ETA: 0s - loss: 0.0239 - accuracy: 0.99 - ETA: 0s - loss: 0.0245 - accuracy: 0.99 - ETA: 0s - loss: 0.0271 - accuracy: 0.99 - ETA: 0s - loss: 0.0286 - accuracy: 0.99 - ETA: 0s - loss: 0.0297 - accuracy: 0.98 - ETA: 0s - loss: 0.0333 - accuracy: 0.98 - 2s 97us/step - loss: 0.0330 - accuracy: 0.9892 - val_loss: 0.0395 - val_accuracy: 0.9862 Epoch 69/100 18068/18068 [==============================] - ETA: 1s - loss: 0.0023 - accuracy: 1.00 - ETA: 0s - loss: 0.0443 - accuracy: 0.98 - ETA: 0s - loss: 0.0333 - accuracy: 0.98 - ETA: 0s - loss: 0.0322 - accuracy: 0.98 - ETA: 0s - loss: 0.0344 - accuracy: 0.98 - ETA: 0s - loss: 0.0348 - accuracy: 0.98 - ETA: 0s - loss: 0.0343 - accuracy: 0.98 - 2s 101us/step - loss: 0.0318 - accuracy: 0.9893 - val_loss: 0.0282 - val_accuracy: 0.9916 Epoch 70/100 18068/18068 [==============================] - ETA: 0s - loss: 0.0045 - accuracy: 1.00 - ETA: 0s - loss: 0.0306 - accuracy: 0.98 - ETA: 0s - loss: 0.0301 - accuracy: 0.98 - ETA: 0s - loss: 0.0355 - accuracy: 0.98 - ETA: 0s - loss: 0.0358 - accuracy: 0.98 - ETA: 0s - loss: 0.0386 - accuracy: 0.98 - ETA: 0s - loss: 0.0364 - accuracy: 0.98 - 2s 101us/step - loss: 0.0350 - accuracy: 0.9886 - val_loss: 0.0411 - val_accuracy: 0.9877 Epoch 71/100 18068/18068 [==============================] - ETA: 0s - loss: 0.1661 - accuracy: 0.96 - ETA: 0s - loss: 0.0207 - accuracy: 0.99 - ETA: 0s - loss: 0.0219 - accuracy: 0.99 - ETA: 0s - loss: 0.0219 - accuracy: 0.99 - ETA: 0s - loss: 0.0232 - accuracy: 0.99 - ETA: 0s - loss: 0.0214 - accuracy: 0.99 - ETA: 0s - loss: 0.0215 - accuracy: 0.99 - 2s 97us/step - loss: 0.0235 - accuracy: 0.9923 - val_loss: 0.0373 - val_accuracy: 0.9881 Epoch 72/100
18068/18068 [==============================] - ETA: 0s - loss: 0.0066 - accuracy: 1.00 - ETA: 0s - loss: 0.0264 - accuracy: 0.99 - ETA: 0s - loss: 0.0317 - accuracy: 0.98 - ETA: 0s - loss: 0.0312 - accuracy: 0.98 - ETA: 0s - loss: 0.0347 - accuracy: 0.98 - ETA: 0s - loss: 0.0337 - accuracy: 0.98 - ETA: 0s - loss: 0.0301 - accuracy: 0.98 - 2s 95us/step - loss: 0.0316 - accuracy: 0.9888 - val_loss: 0.0365 - val_accuracy: 0.9889 Epoch 73/100 18068/18068 [==============================] - ETA: 0s - loss: 0.0025 - accuracy: 1.00 - ETA: 0s - loss: 0.0276 - accuracy: 0.98 - ETA: 0s - loss: 0.0266 - accuracy: 0.99 - ETA: 0s - loss: 0.0272 - accuracy: 0.99 - ETA: 0s - loss: 0.0278 - accuracy: 0.99 - ETA: 0s - loss: 0.0266 - accuracy: 0.99 - ETA: 0s - loss: 0.0274 - accuracy: 0.99 - 2s 97us/step - loss: 0.0304 - accuracy: 0.9897 - val_loss: 0.1574 - val_accuracy: 0.9417 Epoch 74/100 18068/18068 [==============================] - ETA: 1s - loss: 0.1581 - accuracy: 0.93 - ETA: 0s - loss: 0.0238 - accuracy: 0.99 - ETA: 0s - loss: 0.0226 - accuracy: 0.99 - ETA: 0s - loss: 0.0264 - accuracy: 0.99 - ETA: 0s - loss: 0.0258 - accuracy: 0.99 - ETA: 0s - loss: 0.0273 - accuracy: 0.99 - ETA: 0s - loss: 0.0258 - accuracy: 0.99 - 2s 97us/step - loss: 0.0281 - accuracy: 0.9904 - val_loss: 0.0361 - val_accuracy: 0.9866 Epoch 75/100 18068/18068 [==============================] - ETA: 1s - loss: 0.1132 - accuracy: 0.96 - ETA: 0s - loss: 0.0242 - accuracy: 0.99 - ETA: 0s - loss: 0.0260 - accuracy: 0.99 - ETA: 0s - loss: 0.0269 - accuracy: 0.99 - ETA: 0s - loss: 0.0292 - accuracy: 0.99 - ETA: 0s - loss: 0.0281 - accuracy: 0.99 - ETA: 0s - loss: 0.0274 - accuracy: 0.99 - 2s 98us/step - loss: 0.0262 - accuracy: 0.9918 - val_loss: 0.0262 - val_accuracy: 0.9920 Epoch 76/100 18068/18068 [==============================] - ETA: 1s - loss: 0.0058 - accuracy: 1.00 - ETA: 0s - loss: 0.0158 - accuracy: 0.99 - ETA: 0s - loss: 0.0210 - accuracy: 0.99 - ETA: 0s - loss: 0.0223 - accuracy: 0.99 - ETA: 0s - loss: 0.0250 - accuracy: 0.99 - ETA: 0s - loss: 0.0251 - accuracy: 0.99 - ETA: 0s - loss: 0.0277 - accuracy: 0.99 - 2s 94us/step - loss: 0.0280 - accuracy: 0.9905 - val_loss: 0.0269 - val_accuracy: 0.9918 Epoch 77/100 18068/18068 [==============================] - ETA: 1s - loss: 0.0045 - accuracy: 1.00 - ETA: 0s - loss: 0.0264 - accuracy: 0.99 - ETA: 0s - loss: 0.0226 - accuracy: 0.99 - ETA: 0s - loss: 0.0178 - accuracy: 0.99 - ETA: 0s - loss: 0.0208 - accuracy: 0.99 - ETA: 0s - loss: 0.0248 - accuracy: 0.99 - ETA: 0s - loss: 0.0244 - accuracy: 0.99 - 2s 98us/step - loss: 0.0261 - accuracy: 0.9912 - val_loss: 0.0540 - val_accuracy: 0.9809 Epoch 78/100 18068/18068 [==============================] - ETA: 0s - loss: 0.0312 - accuracy: 1.00 - ETA: 0s - loss: 0.0224 - accuracy: 0.99 - ETA: 0s - loss: 0.0276 - accuracy: 0.99 - ETA: 0s - loss: 0.0277 - accuracy: 0.99 - ETA: 0s - loss: 0.0288 - accuracy: 0.99 - ETA: 0s - loss: 0.0275 - accuracy: 0.99 - ETA: 0s - loss: 0.0261 - accuracy: 0.99 - 2s 97us/step - loss: 0.0268 - accuracy: 0.9911 - val_loss: 0.0334 - val_accuracy: 0.9908 Epoch 79/100 18068/18068 [==============================] - ETA: 0s - loss: 0.0066 - accuracy: 1.00 - ETA: 0s - loss: 0.0261 - accuracy: 0.98 - ETA: 0s - loss: 0.0284 - accuracy: 0.98 - ETA: 0s - loss: 0.0250 - accuracy: 0.99 - ETA: 0s - loss: 0.0323 - accuracy: 0.98 - ETA: 0s - loss: 0.0301 - accuracy: 0.99 - ETA: 0s - loss: 0.0281 - accuracy: 0.99 - 2s 94us/step - loss: 0.0277 - accuracy: 0.9910 - val_loss: 0.0764 - val_accuracy: 0.9735 Epoch 80/100 18068/18068 [==============================] - ETA: 0s - loss: 0.0497 - accuracy: 0.96 - ETA: 0s - loss: 0.0316 - accuracy: 0.98 - ETA: 0s - loss: 0.0396 - accuracy: 0.98 - ETA: 0s - loss: 0.0420 - accuracy: 0.98 - ETA: 0s - loss: 0.0374 - accuracy: 0.98 - ETA: 0s - loss: 0.0370 - accuracy: 0.98 - ETA: 0s - loss: 0.0343 - accuracy: 0.98 - 2s 93us/step - loss: 0.0327 - accuracy: 0.9884 - val_loss: 0.0308 - val_accuracy: 0.9921 Epoch 81/100 18068/18068 [==============================] - ETA: 1s - loss: 0.0094 - accuracy: 1.00 - ETA: 0s - loss: 0.0156 - accuracy: 0.99 - ETA: 0s - loss: 0.0212 - accuracy: 0.99 - ETA: 0s - loss: 0.0249 - accuracy: 0.99 - ETA: 0s - loss: 0.0268 - accuracy: 0.99 - ETA: 0s - loss: 0.0242 - accuracy: 0.99 - ETA: 0s - loss: 0.0229 - accuracy: 0.99 - 2s 98us/step - loss: 0.0220 - accuracy: 0.9931 - val_loss: 0.0310 - val_accuracy: 0.9896 Epoch 82/100 18068/18068 [==============================] - ETA: 0s - loss: 5.9038e-04 - accuracy: 1.00 - ETA: 0s - loss: 0.0228 - accuracy: 0.9914 - ETA: 0s - loss: 0.0214 - accuracy: 0.99 - ETA: 0s - loss: 0.0204 - accuracy: 0.99 - ETA: 0s - loss: 0.0228 - accuracy: 0.99 - ETA: 0s - loss: 0.0257 - accuracy: 0.99 - ETA: 0s - loss: 0.0246 - accuracy: 0.99 - 2s 94us/step - loss: 0.0246 - accuracy: 0.9911 - val_loss: 0.0409 - val_accuracy: 0.9863 Epoch 83/100 18068/18068 [==============================] - ETA: 1s - loss: 6.2001e-04 - accuracy: 1.00 - ETA: 0s - loss: 0.0330 - accuracy: 0.9878 - ETA: 0s - loss: 0.0410 - accuracy: 0.98 - ETA: 0s - loss: 0.0346 - accuracy: 0.98 - ETA: 0s - loss: 0.0317 - accuracy: 0.98 - ETA: 0s - loss: 0.0321 - accuracy: 0.98 - ETA: 0s - loss: 0.0305 - accuracy: 0.98 - 2s 97us/step - loss: 0.0286 - accuracy: 0.9901 - val_loss: 0.0233 - val_accuracy: 0.9931 Epoch 84/100 18068/18068 [==============================] - ETA: 0s - loss: 0.0143 - accuracy: 1.00 - ETA: 0s - loss: 0.0310 - accuracy: 0.98 - ETA: 0s - loss: 0.0233 - accuracy: 0.99 - ETA: 0s - loss: 0.0224 - accuracy: 0.99 - ETA: 0s - loss: 0.0250 - accuracy: 0.99 - ETA: 0s - loss: 0.0323 - accuracy: 0.98 - ETA: 0s - loss: 0.0354 - accuracy: 0.98 - 2s 100us/step - loss: 0.0329 - accuracy: 0.9888 - val_loss: 0.0236 - val_accuracy: 0.9920 Epoch 85/100 18068/18068 [==============================] - ETA: 0s - loss: 0.1124 - accuracy: 0.93 - ETA: 0s - loss: 0.0144 - accuracy: 0.99 - ETA: 0s - loss: 0.0157 - accuracy: 0.99 - ETA: 0s - loss: 0.0179 - accuracy: 0.99 - ETA: 0s - loss: 0.0184 - accuracy: 0.99 - ETA: 0s - loss: 0.0179 - accuracy: 0.99 - ETA: 0s - loss: 0.0191 - accuracy: 0.99 - 2s 100us/step - loss: 0.0184 - accuracy: 0.9936 - val_loss: 0.0257 - val_accuracy: 0.9923 Epoch 86/100 18068/18068 [==============================] - ETA: 1s - loss: 0.1240 - accuracy: 0.93 - ETA: 0s - loss: 0.0351 - accuracy: 0.98 - ETA: 0s - loss: 0.0290 - accuracy: 0.98 - ETA: 0s - loss: 0.0258 - accuracy: 0.99 - ETA: 0s - loss: 0.0213 - accuracy: 0.99 - ETA: 0s - loss: 0.0206 - accuracy: 0.99 - ETA: 0s - loss: 0.0288 - accuracy: 0.99 - 2s 93us/step - loss: 0.0303 - accuracy: 0.9896 - val_loss: 0.0298 - val_accuracy: 0.9899 Epoch 87/100 18068/18068 [==============================] - ETA: 0s - loss: 0.0170 - accuracy: 1.00 - ETA: 0s - loss: 0.0520 - accuracy: 0.98 - ETA: 0s - loss: 0.0377 - accuracy: 0.98 - ETA: 0s - loss: 0.0303 - accuracy: 0.98 - ETA: 0s - loss: 0.0280 - accuracy: 0.99 - ETA: 0s - loss: 0.0256 - accuracy: 0.99 - ETA: 0s - loss: 0.0247 - accuracy: 0.99 - 2s 94us/step - loss: 0.0242 - accuracy: 0.9916 - val_loss: 0.0368 - val_accuracy: 0.9900 Epoch 88/100 18068/18068 [==============================] - ETA: 0s - loss: 0.0029 - accuracy: 1.00 - ETA: 0s - loss: 0.0234 - accuracy: 0.99 - ETA: 0s - loss: 0.0309 - accuracy: 0.99 - ETA: 0s - loss: 0.0336 - accuracy: 0.98 - ETA: 0s - loss: 0.0293 - accuracy: 0.99 - ETA: 0s - loss: 0.0262 - accuracy: 0.99 - ETA: 0s - loss: 0.0245 - accuracy: 0.99 - 2s 98us/step - loss: 0.0235 - accuracy: 0.9919 - val_loss: 0.0353 - val_accuracy: 0.9898 Epoch 89/100 18068/18068 [==============================] - ETA: 0s - loss: 0.0204 - accuracy: 1.00 - ETA: 0s - loss: 0.0228 - accuracy: 0.99 - ETA: 0s - loss: 0.0310 - accuracy: 0.98 - ETA: 0s - loss: 0.0281 - accuracy: 0.99 - ETA: 0s - loss: 0.0277 - accuracy: 0.99 - ETA: 0s - loss: 0.0273 - accuracy: 0.99 - ETA: 0s - loss: 0.0280 - accuracy: 0.98 - 2s 96us/step - loss: 0.0293 - accuracy: 0.9896 - val_loss: 0.0315 - val_accuracy: 0.9903 Epoch 90/100
18068/18068 [==============================] - ETA: 1s - loss: 0.0029 - accuracy: 1.00 - ETA: 0s - loss: 0.0310 - accuracy: 0.99 - ETA: 0s - loss: 0.0258 - accuracy: 0.99 - ETA: 0s - loss: 0.0239 - accuracy: 0.99 - ETA: 0s - loss: 0.0220 - accuracy: 0.99 - ETA: 0s - loss: 0.0212 - accuracy: 0.99 - ETA: 0s - loss: 0.0259 - accuracy: 0.99 - 2s 92us/step - loss: 0.0248 - accuracy: 0.9925 - val_loss: 0.0330 - val_accuracy: 0.9895 Epoch 91/100 18068/18068 [==============================] - ETA: 0s - loss: 0.0244 - accuracy: 1.00 - ETA: 0s - loss: 0.0134 - accuracy: 0.99 - ETA: 0s - loss: 0.0164 - accuracy: 0.99 - ETA: 0s - loss: 0.0225 - accuracy: 0.99 - ETA: 0s - loss: 0.0231 - accuracy: 0.99 - ETA: 0s - loss: 0.0249 - accuracy: 0.99 - ETA: 0s - loss: 0.0236 - accuracy: 0.99 - 2s 98us/step - loss: 0.0228 - accuracy: 0.9924 - val_loss: 0.0276 - val_accuracy: 0.9914 Epoch 92/100 18068/18068 [==============================] - ETA: 0s - loss: 0.0057 - accuracy: 1.00 - ETA: 0s - loss: 0.0239 - accuracy: 0.99 - ETA: 0s - loss: 0.0238 - accuracy: 0.99 - ETA: 0s - loss: 0.0214 - accuracy: 0.99 - ETA: 0s - loss: 0.0207 - accuracy: 0.99 - ETA: 0s - loss: 0.0205 - accuracy: 0.99 - ETA: 0s - loss: 0.0212 - accuracy: 0.99 - 2s 99us/step - loss: 0.0213 - accuracy: 0.9927 - val_loss: 0.0323 - val_accuracy: 0.9899 Epoch 93/100 18068/18068 [==============================] - ETA: 1s - loss: 2.3066e-04 - accuracy: 1.00 - ETA: 0s - loss: 0.0247 - accuracy: 0.9918 - ETA: 0s - loss: 0.0236 - accuracy: 0.99 - ETA: 0s - loss: 0.0215 - accuracy: 0.99 - ETA: 0s - loss: 0.0220 - accuracy: 0.99 - ETA: 0s - loss: 0.0200 - accuracy: 0.99 - ETA: 0s - loss: 0.0198 - accuracy: 0.99 - 2s 95us/step - loss: 0.0250 - accuracy: 0.9923 - val_loss: 0.1360 - val_accuracy: 0.9603 Epoch 94/100 18068/18068 [==============================] - ETA: 0s - loss: 0.0579 - accuracy: 0.96 - ETA: 0s - loss: 0.0235 - accuracy: 0.99 - ETA: 0s - loss: 0.0210 - accuracy: 0.99 - ETA: 0s - loss: 0.0253 - accuracy: 0.99 - ETA: 0s - loss: 0.0286 - accuracy: 0.99 - ETA: 0s - loss: 0.0270 - accuracy: 0.99 - ETA: 0s - loss: 0.0251 - accuracy: 0.99 - 2s 97us/step - loss: 0.0238 - accuracy: 0.9920 - val_loss: 0.0203 - val_accuracy: 0.9939 Epoch 95/100 18068/18068 [==============================] - ETA: 1s - loss: 0.0031 - accuracy: 1.00 - ETA: 0s - loss: 0.0116 - accuracy: 0.99 - ETA: 0s - loss: 0.0159 - accuracy: 0.99 - ETA: 0s - loss: 0.0166 - accuracy: 0.99 - ETA: 0s - loss: 0.0207 - accuracy: 0.99 - ETA: 0s - loss: 0.0207 - accuracy: 0.99 - ETA: 0s - loss: 0.0192 - accuracy: 0.99 - 2s 96us/step - loss: 0.0189 - accuracy: 0.9934 - val_loss: 0.0234 - val_accuracy: 0.9933 Epoch 96/100 18068/18068 [==============================] - ETA: 1s - loss: 1.9295e-04 - accuracy: 1.00 - ETA: 0s - loss: 0.0450 - accuracy: 0.9853 - ETA: 0s - loss: 0.0335 - accuracy: 0.98 - ETA: 0s - loss: 0.0266 - accuracy: 0.99 - ETA: 0s - loss: 0.0320 - accuracy: 0.98 - ETA: 0s - loss: 0.0289 - accuracy: 0.99 - ETA: 0s - loss: 0.0261 - accuracy: 0.99 - 2s 95us/step - loss: 0.0259 - accuracy: 0.9911 - val_loss: 0.0221 - val_accuracy: 0.9936 Epoch 97/100 18068/18068 [==============================] - ETA: 1s - loss: 0.0011 - accuracy: 1.00 - ETA: 0s - loss: 0.0193 - accuracy: 0.99 - ETA: 0s - loss: 0.0295 - accuracy: 0.98 - ETA: 0s - loss: 0.0255 - accuracy: 0.99 - ETA: 0s - loss: 0.0218 - accuracy: 0.99 - ETA: 0s - loss: 0.0220 - accuracy: 0.99 - ETA: 0s - loss: 0.0205 - accuracy: 0.99 - 2s 96us/step - loss: 0.0215 - accuracy: 0.9926 - val_loss: 0.0838 - val_accuracy: 0.9744 Epoch 98/100 18068/18068 [==============================] - ETA: 1s - loss: 6.6428e-04 - accuracy: 1.00 - ETA: 0s - loss: 0.0324 - accuracy: 0.9899 - ETA: 0s - loss: 0.0235 - accuracy: 0.99 - ETA: 0s - loss: 0.0230 - accuracy: 0.99 - ETA: 0s - loss: 0.0212 - accuracy: 0.99 - ETA: 0s - loss: 0.0212 - accuracy: 0.99 - ETA: 0s - loss: 0.0256 - accuracy: 0.99 - 2s 94us/step - loss: 0.0280 - accuracy: 0.9908 - val_loss: 0.0490 - val_accuracy: 0.9835 Epoch 99/100 18068/18068 [==============================] - ETA: 1s - loss: 0.0147 - accuracy: 1.00 - ETA: 0s - loss: 0.0185 - accuracy: 0.99 - ETA: 0s - loss: 0.0153 - accuracy: 0.99 - ETA: 0s - loss: 0.0169 - accuracy: 0.99 - ETA: 0s - loss: 0.0163 - accuracy: 0.99 - ETA: 0s - loss: 0.0156 - accuracy: 0.99 - ETA: 0s - loss: 0.0170 - accuracy: 0.99 - 2s 97us/step - loss: 0.0165 - accuracy: 0.9946 - val_loss: 0.0241 - val_accuracy: 0.9921 Epoch 100/100 18068/18068 [==============================] - ETA: 1s - loss: 0.0017 - accuracy: 1.00 - ETA: 0s - loss: 0.0244 - accuracy: 0.99 - ETA: 0s - loss: 0.0232 - accuracy: 0.99 - ETA: 0s - loss: 0.0245 - accuracy: 0.99 - ETA: 0s - loss: 0.0256 - accuracy: 0.99 - ETA: 0s - loss: 0.0261 - accuracy: 0.99 - ETA: 0s - loss: 0.0238 - accuracy: 0.99 - ETA: 0s - loss: 0.0228 - accuracy: 0.99 - 2s 101us/step - loss: 0.0228 - accuracy: 0.9919 - val_loss: 0.0591 - val_accuracy: 0.9803 Initializing network.
100%|████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:00<?, ?it/s]
Round 1.
0%| | 0/100 [00:00<?, ?it/s] 1%|▊ | 1/100 [00:01<02:52, 1.74s/it] 2%|█▋ | 2/100 [00:03<02:51, 1.75s/it] 3%|██▍ | 3/100 [00:05<02:49, 1.75s/it] 4%|███▎ | 4/100 [00:07<02:48, 1.75s/it] 5%|████ | 5/100 [00:08<02:46, 1.75s/it] 6%|████▉ | 6/100 [00:10<02:43, 1.74s/it] 7%|█████▋ | 7/100 [00:12<02:42, 1.74s/it] 8%|██████▌ | 8/100 [00:13<02:39, 1.74s/it] 9%|███████▍ | 9/100 [00:15<02:36, 1.72s/it] 10%|████████ | 10/100 [00:17<02:33, 1.70s/it] 11%|████████▉ | 11/100 [00:18<02:30, 1.69s/it] 12%|█████████▋ | 12/100 [00:20<02:28, 1.69s/it] 13%|██████████▌ | 13/100 [00:22<02:25, 1.68s/it] 14%|███████████▎ | 14/100 [00:23<02:24, 1.68s/it] 15%|████████████▏ | 15/100 [00:25<02:22, 1.68s/it] 16%|████████████▉ | 16/100 [00:27<02:20, 1.68s/it] 17%|█████████████▊ | 17/100 [00:29<02:21, 1.70s/it] 18%|██████████████▌ | 18/100 [00:30<02:18, 1.69s/it] 19%|███████████████▍ | 19/100 [00:32<02:16, 1.68s/it] 20%|████████████████▏ | 20/100 [00:34<02:14, 1.68s/it] 21%|█████████████████ | 21/100 [00:35<02:12, 1.68s/it] 22%|█████████████████▊ | 22/100 [00:37<02:12, 1.69s/it] 23%|██████████████████▋ | 23/100 [00:39<02:09, 1.69s/it] 24%|███████████████████▍ | 24/100 [00:40<02:07, 1.68s/it] 25%|████████████████████▎ | 25/100 [00:42<02:05, 1.68s/it] 26%|█████████████████████ | 26/100 [00:44<02:04, 1.68s/it] 27%|█████████████████████▊ | 27/100 [00:45<02:02, 1.68s/it] 28%|██████████████████████▋ | 28/100 [00:47<02:00, 1.68s/it] 29%|███████████████████████▍ | 29/100 [00:49<01:59, 1.68s/it] 30%|████████████████████████▎ | 30/100 [00:50<01:57, 1.68s/it] 31%|█████████████████████████ | 31/100 [00:52<01:55, 1.68s/it] 32%|█████████████████████████▉ | 32/100 [00:54<01:53, 1.67s/it] 33%|██████████████████████████▋ | 33/100 [00:55<01:51, 1.67s/it] 34%|███████████████████████████▌ | 34/100 [00:57<01:50, 1.67s/it] 35%|████████████████████████████▎ | 35/100 [00:59<01:48, 1.67s/it] 36%|█████████████████████████████▏ | 36/100 [01:00<01:46, 1.67s/it] 37%|█████████████████████████████▉ | 37/100 [01:02<01:45, 1.67s/it] 38%|██████████████████████████████▊ | 38/100 [01:04<01:44, 1.68s/it] 39%|███████████████████████████████▌ | 39/100 [01:05<01:42, 1.68s/it] 40%|████████████████████████████████▍ | 40/100 [01:07<01:40, 1.67s/it] 41%|█████████████████████████████████▏ | 41/100 [01:09<01:38, 1.67s/it] 42%|██████████████████████████████████ | 42/100 [01:10<01:36, 1.67s/it] 43%|██████████████████████████████████▊ | 43/100 [01:12<01:35, 1.68s/it] 44%|███████████████████████████████████▋ | 44/100 [01:14<01:33, 1.67s/it] 45%|████████████████████████████████████▍ | 45/100 [01:15<01:32, 1.68s/it] 46%|█████████████████████████████████████▎ | 46/100 [01:17<01:30, 1.67s/it] 47%|██████████████████████████████████████ | 47/100 [01:19<01:28, 1.68s/it] 48%|██████████████████████████████████████▉ | 48/100 [01:20<01:27, 1.67s/it] 49%|███████████████████████████████████████▋ | 49/100 [01:22<01:25, 1.67s/it] 50%|████████████████████████████████████████▌ | 50/100 [01:24<01:23, 1.67s/it] 51%|█████████████████████████████████████████▎ | 51/100 [01:26<01:21, 1.67s/it] 52%|██████████████████████████████████████████ | 52/100 [01:27<01:20, 1.67s/it] 53%|██████████████████████████████████████████▉ | 53/100 [01:29<01:18, 1.68s/it] 54%|███████████████████████████████████████████▋ | 54/100 [01:31<01:17, 1.68s/it] 55%|████████████████████████████████████████████▌ | 55/100 [01:32<01:15, 1.67s/it] 56%|█████████████████████████████████████████████▎ | 56/100 [01:34<01:13, 1.67s/it] 57%|██████████████████████████████████████████████▏ | 57/100 [01:36<01:11, 1.67s/it] 58%|██████████████████████████████████████████████▉ | 58/100 [01:37<01:10, 1.67s/it] 59%|███████████████████████████████████████████████▊ | 59/100 [01:39<01:08, 1.68s/it] 60%|████████████████████████████████████████████████▌ | 60/100 [01:41<01:07, 1.68s/it] 61%|█████████████████████████████████████████████████▍ | 61/100 [01:42<01:05, 1.68s/it] 62%|██████████████████████████████████████████████████▏ | 62/100 [01:44<01:03, 1.67s/it] 63%|███████████████████████████████████████████████████ | 63/100 [01:46<01:02, 1.68s/it] 64%|███████████████████████████████████████████████████▊ | 64/100 [01:47<01:00, 1.67s/it] 65%|████████████████████████████████████████████████████▋ | 65/100 [01:49<00:58, 1.67s/it] 66%|█████████████████████████████████████████████████████▍ | 66/100 [01:51<00:56, 1.67s/it] 67%|██████████████████████████████████████████████████████▎ | 67/100 [01:52<00:55, 1.69s/it]
68%|███████████████████████████████████████████████████████ | 68/100 [01:54<00:54, 1.69s/it] 69%|███████████████████████████████████████████████████████▉ | 69/100 [01:56<00:53, 1.72s/it] 70%|████████████████████████████████████████████████████████▋ | 70/100 [01:58<00:51, 1.72s/it] 71%|█████████████████████████████████████████████████████████▌ | 71/100 [01:59<00:49, 1.72s/it][Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 1000 out of 1000 | elapsed: 12.0s finished [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 1000 out of 1000 | elapsed: 1.2s finished
the number of miss classified sampels is 236
72%|██████████████████████████████████████████████████████████▎ | 72/100 [02:26<04:21, 9.34s/it] 73%|███████████████████████████████████████████████████████████▏ | 73/100 [02:28<03:10, 7.04s/it] 74%|███████████████████████████████████████████████████████████▉ | 74/100 [02:30<02:21, 5.43s/it] 75%|████████████████████████████████████████████████████████████▊ | 75/100 [02:31<01:47, 4.31s/it] 76%|█████████████████████████████████████████████████████████████▌ | 76/100 [02:33<01:24, 3.51s/it] 77%|██████████████████████████████████████████████████████████████▎ | 77/100 [02:35<01:08, 2.96s/it] 78%|███████████████████████████████████████████████████████████████▏ | 78/100 [02:36<00:56, 2.58s/it] 79%|███████████████████████████████████████████████████████████████▉ | 79/100 [02:38<00:48, 2.30s/it] 80%|████████████████████████████████████████████████████████████████▊ | 80/100 [02:40<00:42, 2.11s/it] 81%|█████████████████████████████████████████████████████████████████▌ | 81/100 [02:41<00:37, 1.98s/it] 82%|██████████████████████████████████████████████████████████████████▍ | 82/100 [02:43<00:33, 1.89s/it] 83%|███████████████████████████████████████████████████████████████████▏ | 83/100 [02:45<00:30, 1.82s/it] 84%|████████████████████████████████████████████████████████████████████ | 84/100 [02:46<00:28, 1.77s/it] 85%|████████████████████████████████████████████████████████████████████▊ | 85/100 [02:48<00:26, 1.74s/it] 86%|█████████████████████████████████████████████████████████████████████▋ | 86/100 [02:50<00:24, 1.72s/it] 87%|██████████████████████████████████████████████████████████████████████▍ | 87/100 [02:51<00:22, 1.70s/it] 88%|███████████████████████████████████████████████████████████████████████▎ | 88/100 [02:53<00:20, 1.70s/it] 89%|████████████████████████████████████████████████████████████████████████ | 89/100 [02:55<00:18, 1.69s/it] 90%|████████████████████████████████████████████████████████████████████████▉ | 90/100 [02:56<00:16, 1.69s/it] 91%|█████████████████████████████████████████████████████████████████████████▋ | 91/100 [02:58<00:15, 1.68s/it] 92%|██████████████████████████████████████████████████████████████████████████▌ | 92/100 [03:00<00:13, 1.68s/it] 93%|███████████████████████████████████████████████████████████████████████████▎ | 93/100 [03:01<00:11, 1.68s/it] 94%|████████████████████████████████████████████████████████████████████████████▏ | 94/100 [03:03<00:10, 1.67s/it] 95%|████████████████████████████████████████████████████████████████████████████▉ | 95/100 [03:05<00:08, 1.67s/it] 96%|█████████████████████████████████████████████████████████████████████████████▊ | 96/100 [03:06<00:06, 1.67s/it] 97%|██████████████████████████████████████████████████████████████████████████████▌ | 97/100 [03:08<00:04, 1.67s/it] 98%|███████████████████████████████████████████████████████████████████████████████▍ | 98/100 [03:10<00:03, 1.66s/it] 99%|████████████████████████████████████████████████████████████████████████████████▏| 99/100 [03:11<00:01, 1.67s/it] 100%|████████████████████████████████████████████████████████████████████████████████| 100/100 [03:13<00:00, 1.94s/it]
Global model loss: 0.19349391767446147; global model accuracy: 0.9243025779724121 Round 2.
0%| | 0/100 [00:00<?, ?it/s] 1%|▊ | 1/100 [00:01<02:43, 1.66s/it] 2%|█▋ | 2/100 [00:03<02:42, 1.66s/it] 3%|██▍ | 3/100 [00:04<02:40, 1.66s/it] 4%|███▎ | 4/100 [00:06<02:39, 1.66s/it] 5%|████ | 5/100 [00:08<02:37, 1.66s/it] 6%|████▉ | 6/100 [00:09<02:36, 1.66s/it] 7%|█████▋ | 7/100 [00:11<02:34, 1.66s/it] 8%|██████▌ | 8/100 [00:13<02:32, 1.66s/it] 9%|███████▍ | 9/100 [00:14<02:30, 1.65s/it] 10%|████████ | 10/100 [00:16<02:28, 1.65s/it] 11%|████████▉ | 11/100 [00:18<02:27, 1.66s/it] 12%|█████████▋ | 12/100 [00:19<02:26, 1.66s/it] 13%|██████████▌ | 13/100 [00:21<02:24, 1.66s/it] 14%|███████████▎ | 14/100 [00:23<02:22, 1.66s/it] 15%|████████████▏ | 15/100 [00:24<02:21, 1.66s/it] 16%|████████████▉ | 16/100 [00:26<02:19, 1.66s/it] 17%|█████████████▊ | 17/100 [00:28<02:18, 1.67s/it] 18%|██████████████▌ | 18/100 [00:29<02:16, 1.67s/it] 19%|███████████████▍ | 19/100 [00:31<02:14, 1.66s/it] 20%|████████████████▏ | 20/100 [00:33<02:13, 1.66s/it] 21%|█████████████████ | 21/100 [00:34<02:11, 1.66s/it] 22%|█████████████████▊ | 22/100 [00:36<02:09, 1.66s/it] 23%|██████████████████▋ | 23/100 [00:38<02:07, 1.66s/it] 24%|███████████████████▍ | 24/100 [00:39<02:05, 1.66s/it] 25%|████████████████████▎ | 25/100 [00:41<02:04, 1.66s/it] 26%|█████████████████████ | 26/100 [00:43<02:03, 1.66s/it] 27%|█████████████████████▊ | 27/100 [00:44<02:01, 1.66s/it] 28%|██████████████████████▋ | 28/100 [00:46<01:59, 1.66s/it] 29%|███████████████████████▍ | 29/100 [00:48<01:57, 1.66s/it] 30%|████████████████████████▎ | 30/100 [00:49<01:55, 1.66s/it] 31%|█████████████████████████ | 31/100 [00:51<01:54, 1.66s/it] 32%|█████████████████████████▉ | 32/100 [00:53<01:52, 1.65s/it] 33%|██████████████████████████▋ | 33/100 [00:54<01:50, 1.65s/it] 34%|███████████████████████████▌ | 34/100 [00:56<01:49, 1.65s/it] 35%|████████████████████████████▎ | 35/100 [00:58<01:47, 1.65s/it] 36%|█████████████████████████████▏ | 36/100 [00:59<01:46, 1.66s/it] 37%|█████████████████████████████▉ | 37/100 [01:01<01:44, 1.67s/it] 38%|██████████████████████████████▊ | 38/100 [01:03<01:43, 1.67s/it] 39%|███████████████████████████████▌ | 39/100 [01:04<01:42, 1.67s/it] 40%|████████████████████████████████▍ | 40/100 [01:06<01:40, 1.67s/it] 41%|█████████████████████████████████▏ | 41/100 [01:08<01:38, 1.66s/it] 42%|██████████████████████████████████ | 42/100 [01:09<01:36, 1.66s/it] 43%|██████████████████████████████████▊ | 43/100 [01:11<01:34, 1.65s/it] 44%|███████████████████████████████████▋ | 44/100 [01:13<01:32, 1.65s/it] 45%|████████████████████████████████████▍ | 45/100 [01:14<01:30, 1.65s/it] 46%|█████████████████████████████████████▎ | 46/100 [01:16<01:29, 1.65s/it] 47%|██████████████████████████████████████ | 47/100 [01:17<01:27, 1.65s/it] 48%|██████████████████████████████████████▉ | 48/100 [01:19<01:26, 1.66s/it] 49%|███████████████████████████████████████▋ | 49/100 [01:21<01:24, 1.66s/it] 50%|████████████████████████████████████████▌ | 50/100 [01:22<01:23, 1.66s/it] 51%|█████████████████████████████████████████▎ | 51/100 [01:24<01:21, 1.66s/it] 52%|██████████████████████████████████████████ | 52/100 [01:26<01:19, 1.66s/it] 53%|██████████████████████████████████████████▉ | 53/100 [01:27<01:18, 1.67s/it] 54%|███████████████████████████████████████████▋ | 54/100 [01:29<01:16, 1.66s/it] 55%|████████████████████████████████████████████▌ | 55/100 [01:31<01:14, 1.66s/it] 56%|█████████████████████████████████████████████▎ | 56/100 [01:32<01:13, 1.66s/it] 57%|██████████████████████████████████████████████▏ | 57/100 [01:34<01:11, 1.66s/it] 58%|██████████████████████████████████████████████▉ | 58/100 [01:36<01:09, 1.66s/it] 59%|███████████████████████████████████████████████▊ | 59/100 [01:37<01:07, 1.66s/it] 60%|████████████████████████████████████████████████▌ | 60/100 [01:39<01:06, 1.66s/it] 61%|█████████████████████████████████████████████████▍ | 61/100 [01:41<01:04, 1.66s/it] 62%|██████████████████████████████████████████████████▏ | 62/100 [01:42<01:03, 1.66s/it] 63%|███████████████████████████████████████████████████ | 63/100 [01:44<01:01, 1.66s/it] 64%|███████████████████████████████████████████████████▊ | 64/100 [01:46<00:59, 1.66s/it] 65%|████████████████████████████████████████████████████▋ | 65/100 [01:47<00:58, 1.66s/it] 66%|█████████████████████████████████████████████████████▍ | 66/100 [01:49<00:56, 1.66s/it] 67%|██████████████████████████████████████████████████████▎ | 67/100 [01:51<00:54, 1.66s/it]
68%|███████████████████████████████████████████████████████ | 68/100 [01:52<00:52, 1.66s/it] 69%|███████████████████████████████████████████████████████▉ | 69/100 [01:54<00:51, 1.67s/it] 70%|████████████████████████████████████████████████████████▋ | 70/100 [01:56<00:49, 1.67s/it] 71%|█████████████████████████████████████████████████████████▌ | 71/100 [01:57<00:48, 1.67s/it] 72%|██████████████████████████████████████████████████████████▎ | 72/100 [01:59<00:46, 1.67s/it] 73%|███████████████████████████████████████████████████████████▏ | 73/100 [02:01<00:44, 1.66s/it] 74%|███████████████████████████████████████████████████████████▉ | 74/100 [02:02<00:43, 1.66s/it] 75%|████████████████████████████████████████████████████████████▊ | 75/100 [02:04<00:41, 1.66s/it] 76%|█████████████████████████████████████████████████████████████▌ | 76/100 [02:06<00:39, 1.66s/it] 77%|██████████████████████████████████████████████████████████████▎ | 77/100 [02:07<00:38, 1.66s/it] 78%|███████████████████████████████████████████████████████████████▏ | 78/100 [02:09<00:36, 1.66s/it] 79%|███████████████████████████████████████████████████████████████▉ | 79/100 [02:11<00:34, 1.66s/it] 80%|████████████████████████████████████████████████████████████████▊ | 80/100 [02:12<00:33, 1.66s/it] 81%|█████████████████████████████████████████████████████████████████▌ | 81/100 [02:14<00:31, 1.66s/it] 82%|██████████████████████████████████████████████████████████████████▍ | 82/100 [02:16<00:29, 1.66s/it] 83%|███████████████████████████████████████████████████████████████████▏ | 83/100 [02:17<00:28, 1.66s/it] 84%|████████████████████████████████████████████████████████████████████ | 84/100 [02:19<00:26, 1.66s/it][Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 1000 out of 1000 | elapsed: 1.2s finished
the number of miss classified sampels is 1014
85%|████████████████████████████████████████████████████████████████████▊ | 85/100 [03:13<04:22, 17.51s/it] 86%|█████████████████████████████████████████████████████████████████████▋ | 86/100 [03:15<02:58, 12.75s/it] 87%|██████████████████████████████████████████████████████████████████████▍ | 87/100 [03:17<02:02, 9.43s/it] 88%|███████████████████████████████████████████████████████████████████████▎ | 88/100 [03:18<01:25, 7.10s/it] 89%|████████████████████████████████████████████████████████████████████████ | 89/100 [03:20<01:00, 5.47s/it] 90%|████████████████████████████████████████████████████████████████████████▉ | 90/100 [03:22<00:43, 4.32s/it] 91%|█████████████████████████████████████████████████████████████████████████▋ | 91/100 [03:23<00:31, 3.52s/it] 92%|██████████████████████████████████████████████████████████████████████████▌ | 92/100 [03:25<00:23, 2.96s/it] 93%|███████████████████████████████████████████████████████████████████████████▎ | 93/100 [03:27<00:17, 2.57s/it] 94%|████████████████████████████████████████████████████████████████████████████▏ | 94/100 [03:28<00:13, 2.29s/it] 95%|████████████████████████████████████████████████████████████████████████████▉ | 95/100 [03:30<00:10, 2.10s/it] 96%|█████████████████████████████████████████████████████████████████████████████▊ | 96/100 [03:32<00:07, 1.98s/it] 97%|██████████████████████████████████████████████████████████████████████████████▌ | 97/100 [03:33<00:05, 1.88s/it] 98%|███████████████████████████████████████████████████████████████████████████████▍ | 98/100 [03:35<00:03, 1.81s/it] 99%|████████████████████████████████████████████████████████████████████████████████▏| 99/100 [03:37<00:01, 1.76s/it] 100%|████████████████████████████████████████████████████████████████████████████████| 100/100 [03:38<00:00, 2.19s/it]
Global model loss: 0.056399353472181495; global model accuracy: 0.9811399579048157 Round 3.
0%| | 0/100 [00:00<?, ?it/s] 1%|▊ | 1/100 [00:01<02:45, 1.67s/it] 2%|█▋ | 2/100 [00:03<02:43, 1.67s/it] 3%|██▍ | 3/100 [00:04<02:41, 1.66s/it] 4%|███▎ | 4/100 [00:06<02:39, 1.66s/it] 5%|████ | 5/100 [00:08<02:37, 1.66s/it] 6%|████▉ | 6/100 [00:09<02:35, 1.66s/it] 7%|█████▋ | 7/100 [00:11<02:34, 1.66s/it] 8%|██████▌ | 8/100 [00:13<02:32, 1.65s/it] 9%|███████▍ | 9/100 [00:14<02:30, 1.65s/it] 10%|████████ | 10/100 [00:16<02:28, 1.65s/it] 11%|████████▉ | 11/100 [00:18<02:26, 1.65s/it] 12%|█████████▋ | 12/100 [00:19<02:25, 1.66s/it] 13%|██████████▌ | 13/100 [00:21<02:24, 1.66s/it] 14%|███████████▎ | 14/100 [00:23<02:22, 1.66s/it] 15%|████████████▏ | 15/100 [00:24<02:21, 1.66s/it] 16%|████████████▉ | 16/100 [00:26<02:19, 1.66s/it] 17%|█████████████▊ | 17/100 [00:28<02:17, 1.66s/it] 18%|██████████████▌ | 18/100 [00:29<02:15, 1.66s/it] 19%|███████████████▍ | 19/100 [00:31<02:14, 1.66s/it] 20%|████████████████▏ | 20/100 [00:33<02:13, 1.67s/it] 21%|█████████████████ | 21/100 [00:34<02:11, 1.66s/it] 22%|█████████████████▊ | 22/100 [00:36<02:09, 1.66s/it] 23%|██████████████████▋ | 23/100 [00:38<02:07, 1.66s/it] 24%|███████████████████▍ | 24/100 [00:39<02:06, 1.66s/it] 25%|████████████████████▎ | 25/100 [00:41<02:04, 1.66s/it] 26%|█████████████████████ | 26/100 [00:43<02:03, 1.66s/it] 27%|█████████████████████▊ | 27/100 [00:44<02:01, 1.66s/it][Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 1000 out of 1000 | elapsed: 1.2s finished
the number of miss classified sampels is 230
28%|██████████████████████▋ | 28/100 [00:59<06:31, 5.44s/it] 29%|███████████████████████▍ | 29/100 [01:00<05:05, 4.30s/it] 30%|████████████████████████▎ | 30/100 [01:02<04:06, 3.51s/it] 31%|█████████████████████████ | 31/100 [01:04<03:23, 2.96s/it] 32%|█████████████████████████▉ | 32/100 [01:05<02:54, 2.57s/it] 33%|██████████████████████████▋ | 33/100 [01:07<02:33, 2.30s/it] 34%|███████████████████████████▌ | 34/100 [01:08<02:18, 2.10s/it] 35%|████████████████████████████▎ | 35/100 [01:10<02:07, 1.97s/it] 36%|█████████████████████████████▏ | 36/100 [01:12<02:00, 1.89s/it] 37%|█████████████████████████████▉ | 37/100 [01:14<01:54, 1.82s/it] 38%|██████████████████████████████▊ | 38/100 [01:15<01:49, 1.77s/it] 39%|███████████████████████████████▌ | 39/100 [01:17<01:45, 1.73s/it] 40%|████████████████████████████████▍ | 40/100 [01:18<01:41, 1.70s/it] 41%|█████████████████████████████████▏ | 41/100 [01:20<01:39, 1.69s/it] 42%|██████████████████████████████████ | 42/100 [01:22<01:36, 1.67s/it] 43%|██████████████████████████████████▊ | 43/100 [01:23<01:34, 1.66s/it] 44%|███████████████████████████████████▋ | 44/100 [01:25<01:32, 1.66s/it] 45%|████████████████████████████████████▍ | 45/100 [01:27<01:32, 1.68s/it] 46%|█████████████████████████████████████▎ | 46/100 [01:28<01:30, 1.67s/it] 47%|██████████████████████████████████████ | 47/100 [01:30<01:28, 1.67s/it] 48%|██████████████████████████████████████▉ | 48/100 [01:32<01:26, 1.66s/it] 49%|███████████████████████████████████████▋ | 49/100 [01:33<01:24, 1.65s/it] 50%|████████████████████████████████████████▌ | 50/100 [01:35<01:22, 1.65s/it] 51%|█████████████████████████████████████████▎ | 51/100 [01:37<01:21, 1.66s/it] 52%|██████████████████████████████████████████ | 52/100 [01:38<01:19, 1.65s/it] 53%|██████████████████████████████████████████▉ | 53/100 [01:40<01:17, 1.65s/it] 54%|███████████████████████████████████████████▋ | 54/100 [01:42<01:15, 1.65s/it] 55%|████████████████████████████████████████████▌ | 55/100 [01:43<01:14, 1.65s/it] 56%|█████████████████████████████████████████████▎ | 56/100 [01:45<01:12, 1.65s/it] 57%|██████████████████████████████████████████████▏ | 57/100 [01:46<01:10, 1.64s/it] 58%|██████████████████████████████████████████████▉ | 58/100 [01:48<01:08, 1.64s/it] 59%|███████████████████████████████████████████████▊ | 59/100 [01:50<01:07, 1.64s/it] 60%|████████████████████████████████████████████████▌ | 60/100 [01:51<01:05, 1.64s/it] 61%|█████████████████████████████████████████████████▍ | 61/100 [01:53<01:04, 1.65s/it] 62%|██████████████████████████████████████████████████▏ | 62/100 [01:55<01:02, 1.65s/it] 63%|███████████████████████████████████████████████████ | 63/100 [01:56<01:00, 1.64s/it] 64%|███████████████████████████████████████████████████▊ | 64/100 [01:58<00:59, 1.64s/it] 65%|████████████████████████████████████████████████████▋ | 65/100 [02:00<00:57, 1.64s/it] 66%|█████████████████████████████████████████████████████▍ | 66/100 [02:01<00:55, 1.64s/it] 67%|██████████████████████████████████████████████████████▎ | 67/100 [02:03<00:54, 1.64s/it] 68%|███████████████████████████████████████████████████████ | 68/100 [02:05<00:52, 1.64s/it] 69%|███████████████████████████████████████████████████████▉ | 69/100 [02:06<00:50, 1.64s/it] 70%|████████████████████████████████████████████████████████▋ | 70/100 [02:08<00:49, 1.65s/it] 71%|█████████████████████████████████████████████████████████▌ | 71/100 [02:10<00:47, 1.65s/it] 72%|██████████████████████████████████████████████████████████▎ | 72/100 [02:11<00:46, 1.65s/it] 73%|███████████████████████████████████████████████████████████▏ | 73/100 [02:13<00:44, 1.64s/it] 74%|███████████████████████████████████████████████████████████▉ | 74/100 [02:14<00:42, 1.64s/it] 75%|████████████████████████████████████████████████████████████▊ | 75/100 [02:16<00:41, 1.65s/it] 76%|█████████████████████████████████████████████████████████████▌ | 76/100 [02:18<00:39, 1.65s/it] 77%|██████████████████████████████████████████████████████████████▎ | 77/100 [02:19<00:37, 1.64s/it] 78%|███████████████████████████████████████████████████████████████▏ | 78/100 [02:21<00:36, 1.65s/it] 79%|███████████████████████████████████████████████████████████████▉ | 79/100 [02:23<00:34, 1.65s/it] 80%|████████████████████████████████████████████████████████████████▊ | 80/100 [02:24<00:32, 1.65s/it] 81%|█████████████████████████████████████████████████████████████████▌ | 81/100 [02:26<00:31, 1.65s/it] 82%|██████████████████████████████████████████████████████████████████▍ | 82/100 [02:28<00:29, 1.65s/it] 83%|███████████████████████████████████████████████████████████████████▏ | 83/100 [02:29<00:27, 1.65s/it] 84%|████████████████████████████████████████████████████████████████████ | 84/100 [02:31<00:26, 1.64s/it] 85%|████████████████████████████████████████████████████████████████████▊ | 85/100 [02:33<00:24, 1.65s/it] 86%|█████████████████████████████████████████████████████████████████████▋ | 86/100 [02:34<00:23, 1.65s/it] 87%|██████████████████████████████████████████████████████████████████████▍ | 87/100 [02:36<00:21, 1.65s/it] 88%|███████████████████████████████████████████████████████████████████████▎ | 88/100 [02:38<00:19, 1.65s/it] 89%|████████████████████████████████████████████████████████████████████████ | 89/100 [02:39<00:18, 1.65s/it] 90%|████████████████████████████████████████████████████████████████████████▉ | 90/100 [02:41<00:16, 1.65s/it] 91%|█████████████████████████████████████████████████████████████████████████▋ | 91/100 [02:42<00:14, 1.65s/it] 92%|██████████████████████████████████████████████████████████████████████████▌ | 92/100 [02:44<00:13, 1.65s/it] 93%|███████████████████████████████████████████████████████████████████████████▎ | 93/100 [02:46<00:11, 1.65s/it] 94%|████████████████████████████████████████████████████████████████████████████▏ | 94/100 [02:47<00:09, 1.65s/it] 95%|████████████████████████████████████████████████████████████████████████████▉ | 95/100 [02:49<00:08, 1.65s/it]
96%|█████████████████████████████████████████████████████████████████████████████▊ | 96/100 [02:51<00:06, 1.65s/it] 97%|██████████████████████████████████████████████████████████████████████████████▌ | 97/100 [02:52<00:04, 1.65s/it] 98%|███████████████████████████████████████████████████████████████████████████████▍ | 98/100 [02:54<00:03, 1.65s/it] 99%|████████████████████████████████████████████████████████████████████████████████▏| 99/100 [02:56<00:01, 1.65s/it] 100%|████████████████████████████████████████████████████████████████████████████████| 100/100 [02:57<00:00, 1.78s/it]
Global model loss: 0.08008340218102815; global model accuracy: 0.9698019027709961 Round 4.
0%| | 0/100 [00:00<?, ?it/s][Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 1000 out of 1000 | elapsed: 1.2s finished
the number of miss classified sampels is 391
100%|████████████████████████████████████████████████████████████████████████████████| 100/100 [00:22<00:00, 4.48it/s]
Global model loss: 0.805769821976991; global model accuracy: 0.7652534246444702 Round 5.
0%| | 0/100 [00:00<?, ?it/s][Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 1000 out of 1000 | elapsed: 1.2s finished
the number of miss classified sampels is 2932
100%|████████████████████████████████████████████████████████████████████████████████| 100/100 [02:28<00:00, 1.49s/it]
Global model loss: 2.228477299086053; global model accuracy: 0.646946370601654 Round 6.
0%| | 0/100 [00:00<?, ?it/s][Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 1000 out of 1000 | elapsed: 1.2s finished
the number of miss classified sampels is 4490
100%|████████████████████████████████████████████████████████████████████████████████| 100/100 [03:46<00:00, 2.26s/it]
Global model loss: 4.57180075867313; global model accuracy: 0.5873737335205078 Round 7.
0%| | 0/100 [00:00<?, ?it/s][Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 1000 out of 1000 | elapsed: 1.2s finished
the number of miss classified sampels is 5231
100%|████████████████████████████████████████████████████████████████████████████████| 100/100 [04:32<00:00, 2.72s/it]
Global model loss: 7.691132786578953; global model accuracy: 0.5873737335205078 Round 8.
0%| | 0/100 [00:00<?, ?it/s] 1%|▊ | 1/100 [00:01<02:57, 1.79s/it] 2%|█▋ | 2/100 [00:03<02:55, 1.79s/it] 3%|██▍ | 3/100 [00:05<02:53, 1.78s/it] 4%|███▎ | 4/100 [00:07<02:52, 1.80s/it] 5%|████ | 5/100 [00:08<02:50, 1.79s/it] 6%|████▉ | 6/100 [00:10<02:47, 1.78s/it] 7%|█████▋ | 7/100 [00:12<02:44, 1.77s/it] 8%|██████▌ | 8/100 [00:14<02:42, 1.76s/it] 9%|███████▍ | 9/100 [00:15<02:40, 1.77s/it] 10%|████████ | 10/100 [00:17<02:38, 1.77s/it] 11%|████████▉ | 11/100 [00:19<02:37, 1.77s/it] 12%|█████████▋ | 12/100 [00:21<02:36, 1.77s/it] 13%|██████████▌ | 13/100 [00:23<02:34, 1.77s/it] 14%|███████████▎ | 14/100 [00:24<02:32, 1.78s/it] 15%|████████████▏ | 15/100 [00:26<02:30, 1.77s/it] 16%|████████████▉ | 16/100 [00:28<02:29, 1.77s/it] 17%|█████████████▊ | 17/100 [00:30<02:27, 1.77s/it] 18%|██████████████▌ | 18/100 [00:31<02:25, 1.77s/it] 19%|███████████████▍ | 19/100 [00:33<02:22, 1.76s/it] 20%|████████████████▏ | 20/100 [00:35<02:20, 1.76s/it] 21%|█████████████████ | 21/100 [00:37<02:20, 1.77s/it] 22%|█████████████████▊ | 22/100 [00:39<02:19, 1.79s/it] 23%|██████████████████▋ | 23/100 [00:40<02:17, 1.78s/it] 24%|███████████████████▍ | 24/100 [00:42<02:15, 1.78s/it] 25%|████████████████████▎ | 25/100 [00:44<02:13, 1.77s/it] 26%|█████████████████████ | 26/100 [00:46<02:10, 1.77s/it] 27%|█████████████████████▊ | 27/100 [00:47<02:08, 1.76s/it] 28%|██████████████████████▋ | 28/100 [00:49<02:06, 1.76s/it] 29%|███████████████████████▍ | 29/100 [00:51<02:04, 1.76s/it] 30%|████████████████████████▎ | 30/100 [00:53<02:03, 1.76s/it] 31%|█████████████████████████ | 31/100 [00:54<02:02, 1.77s/it] 32%|█████████████████████████▉ | 32/100 [00:56<02:00, 1.77s/it] 33%|██████████████████████████▋ | 33/100 [00:58<01:57, 1.76s/it] 34%|███████████████████████████▌ | 34/100 [01:00<01:56, 1.77s/it] 35%|████████████████████████████▎ | 35/100 [01:01<01:54, 1.76s/it] 36%|█████████████████████████████▏ | 36/100 [01:03<01:52, 1.76s/it] 37%|█████████████████████████████▉ | 37/100 [01:05<01:50, 1.76s/it] 38%|██████████████████████████████▊ | 38/100 [01:07<01:48, 1.75s/it] 39%|███████████████████████████████▌ | 39/100 [01:08<01:46, 1.75s/it] 40%|████████████████████████████████▍ | 40/100 [01:10<01:45, 1.75s/it] 41%|█████████████████████████████████▏ | 41/100 [01:12<01:43, 1.75s/it] 42%|██████████████████████████████████ | 42/100 [01:14<01:42, 1.78s/it] 43%|██████████████████████████████████▊ | 43/100 [01:16<01:41, 1.77s/it] 44%|███████████████████████████████████▋ | 44/100 [01:17<01:39, 1.77s/it] 45%|████████████████████████████████████▍ | 45/100 [01:19<01:36, 1.76s/it] 46%|█████████████████████████████████████▎ | 46/100 [01:21<01:35, 1.76s/it] 47%|██████████████████████████████████████ | 47/100 [01:23<01:33, 1.76s/it] 48%|██████████████████████████████████████▉ | 48/100 [01:24<01:31, 1.76s/it] 49%|███████████████████████████████████████▋ | 49/100 [01:26<01:29, 1.76s/it] 50%|████████████████████████████████████████▌ | 50/100 [01:28<01:27, 1.76s/it] 51%|█████████████████████████████████████████▎ | 51/100 [01:30<01:26, 1.76s/it] 52%|██████████████████████████████████████████ | 52/100 [01:31<01:24, 1.76s/it] 53%|██████████████████████████████████████████▉ | 53/100 [01:33<01:22, 1.76s/it] 54%|███████████████████████████████████████████▋ | 54/100 [01:35<01:21, 1.77s/it] 55%|████████████████████████████████████████████▌ | 55/100 [01:37<01:19, 1.77s/it] 56%|█████████████████████████████████████████████▎ | 56/100 [01:38<01:17, 1.77s/it] 57%|██████████████████████████████████████████████▏ | 57/100 [01:40<01:15, 1.76s/it] 58%|██████████████████████████████████████████████▉ | 58/100 [01:42<01:13, 1.76s/it] 59%|███████████████████████████████████████████████▊ | 59/100 [01:44<01:12, 1.76s/it] 60%|████████████████████████████████████████████████▌ | 60/100 [01:46<01:10, 1.76s/it] 61%|█████████████████████████████████████████████████▍ | 61/100 [01:47<01:08, 1.77s/it] 62%|██████████████████████████████████████████████████▏ | 62/100 [01:49<01:07, 1.77s/it] 63%|███████████████████████████████████████████████████ | 63/100 [01:51<01:05, 1.77s/it] 64%|███████████████████████████████████████████████████▊ | 64/100 [01:53<01:03, 1.77s/it] 65%|████████████████████████████████████████████████████▋ | 65/100 [01:54<01:02, 1.78s/it] 66%|█████████████████████████████████████████████████████▍ | 66/100 [01:56<01:00, 1.77s/it] 67%|██████████████████████████████████████████████████████▎ | 67/100 [01:58<00:57, 1.76s/it]
68%|███████████████████████████████████████████████████████ | 68/100 [02:00<00:55, 1.75s/it] 69%|███████████████████████████████████████████████████████▉ | 69/100 [02:01<00:54, 1.75s/it] 70%|████████████████████████████████████████████████████████▋ | 70/100 [02:03<00:52, 1.75s/it] 71%|█████████████████████████████████████████████████████████▌ | 71/100 [02:05<00:50, 1.75s/it] 72%|██████████████████████████████████████████████████████████▎ | 72/100 [02:07<00:49, 1.75s/it] 73%|███████████████████████████████████████████████████████████▏ | 73/100 [02:08<00:47, 1.75s/it] 74%|███████████████████████████████████████████████████████████▉ | 74/100 [02:10<00:45, 1.74s/it] 75%|████████████████████████████████████████████████████████████▊ | 75/100 [02:12<00:43, 1.75s/it] 76%|█████████████████████████████████████████████████████████████▌ | 76/100 [02:14<00:41, 1.75s/it] 77%|██████████████████████████████████████████████████████████████▎ | 77/100 [02:15<00:39, 1.71s/it] 78%|███████████████████████████████████████████████████████████████▏ | 78/100 [02:17<00:37, 1.69s/it][Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 1000 out of 1000 | elapsed: 1.2s finished
the number of miss classified sampels is 5199
79%|███████████████████████████████████████████████████████████████▉ | 79/100 [06:44<28:29, 81.39s/it] 80%|████████████████████████████████████████████████████████████████▊ | 80/100 [06:46<19:09, 57.49s/it] 81%|█████████████████████████████████████████████████████████████████▌ | 81/100 [06:48<12:54, 40.74s/it] 82%|██████████████████████████████████████████████████████████████████▍ | 82/100 [06:49<08:42, 29.02s/it] 83%|███████████████████████████████████████████████████████████████████▏ | 83/100 [06:51<05:53, 20.82s/it] 84%|████████████████████████████████████████████████████████████████████ | 84/100 [06:53<04:01, 15.08s/it] 85%|████████████████████████████████████████████████████████████████████▊ | 85/100 [06:54<02:45, 11.05s/it] 86%|█████████████████████████████████████████████████████████████████████▋ | 86/100 [06:56<01:55, 8.24s/it] 87%|██████████████████████████████████████████████████████████████████████▍ | 87/100 [06:58<01:21, 6.27s/it] 88%|███████████████████████████████████████████████████████████████████████▎ | 88/100 [06:59<00:58, 4.89s/it] 89%|████████████████████████████████████████████████████████████████████████ | 89/100 [07:01<00:43, 3.93s/it] 90%|████████████████████████████████████████████████████████████████████████▉ | 90/100 [07:03<00:32, 3.24s/it] 91%|█████████████████████████████████████████████████████████████████████████▋ | 91/100 [07:04<00:24, 2.77s/it] 92%|██████████████████████████████████████████████████████████████████████████▌ | 92/100 [07:06<00:19, 2.44s/it] 93%|███████████████████████████████████████████████████████████████████████████▎ | 93/100 [07:08<00:15, 2.21s/it] 94%|████████████████████████████████████████████████████████████████████████████▏ | 94/100 [07:09<00:12, 2.05s/it] 95%|████████████████████████████████████████████████████████████████████████████▉ | 95/100 [07:11<00:09, 1.94s/it] 96%|█████████████████████████████████████████████████████████████████████████████▊ | 96/100 [07:13<00:07, 1.86s/it] 97%|██████████████████████████████████████████████████████████████████████████████▌ | 97/100 [07:14<00:05, 1.81s/it] 98%|███████████████████████████████████████████████████████████████████████████████▍ | 98/100 [07:16<00:03, 1.77s/it] 99%|████████████████████████████████████████████████████████████████████████████████▏| 99/100 [07:18<00:01, 1.74s/it] 100%|████████████████████████████████████████████████████████████████████████████████| 100/100 [07:19<00:00, 4.40s/it]
Global model loss: 1.8344421140176637; global model accuracy: 0.7665107846260071 Round 9.
0%| | 0/100 [00:00<?, ?it/s] 1%|▊ | 1/100 [00:01<02:56, 1.78s/it] 2%|█▋ | 2/100 [00:03<02:55, 1.79s/it] 3%|██▍ | 3/100 [00:05<02:54, 1.80s/it] 4%|███▎ | 4/100 [00:07<02:52, 1.79s/it] 5%|████ | 5/100 [00:08<02:50, 1.79s/it] 6%|████▉ | 6/100 [00:10<02:48, 1.79s/it] 7%|█████▋ | 7/100 [00:12<02:45, 1.78s/it] 8%|██████▌ | 8/100 [00:14<02:40, 1.75s/it] 9%|███████▍ | 9/100 [00:15<02:37, 1.73s/it] 10%|████████ | 10/100 [00:17<02:34, 1.72s/it] 11%|████████▉ | 11/100 [00:19<02:32, 1.71s/it] 12%|█████████▋ | 12/100 [00:21<02:31, 1.72s/it] 13%|██████████▌ | 13/100 [00:22<02:28, 1.71s/it] 14%|███████████▎ | 14/100 [00:24<02:27, 1.71s/it] 15%|████████████▏ | 15/100 [00:26<02:25, 1.71s/it] 16%|████████████▉ | 16/100 [00:27<02:21, 1.69s/it] 17%|█████████████▊ | 17/100 [00:29<02:19, 1.69s/it] 18%|██████████████▌ | 18/100 [00:31<02:17, 1.68s/it] 19%|███████████████▍ | 19/100 [00:32<02:16, 1.69s/it] 20%|████████████████▏ | 20/100 [00:34<02:16, 1.71s/it] 21%|█████████████████ | 21/100 [00:36<02:17, 1.73s/it] 22%|█████████████████▊ | 22/100 [00:38<02:16, 1.75s/it] 23%|██████████████████▋ | 23/100 [00:39<02:15, 1.76s/it] 24%|███████████████████▍ | 24/100 [00:41<02:14, 1.77s/it] 25%|████████████████████▎ | 25/100 [00:43<02:12, 1.77s/it] 26%|█████████████████████ | 26/100 [00:45<02:08, 1.74s/it] 27%|█████████████████████▊ | 27/100 [00:46<02:05, 1.72s/it] 28%|██████████████████████▋ | 28/100 [00:48<02:02, 1.70s/it] 29%|███████████████████████▍ | 29/100 [00:50<01:59, 1.69s/it] 30%|████████████████████████▎ | 30/100 [00:51<01:57, 1.69s/it] 31%|█████████████████████████ | 31/100 [00:53<01:56, 1.69s/it] 32%|█████████████████████████▉ | 32/100 [00:55<01:54, 1.69s/it] 33%|██████████████████████████▋ | 33/100 [00:56<01:52, 1.68s/it] 34%|███████████████████████████▌ | 34/100 [00:58<01:50, 1.68s/it] 35%|████████████████████████████▎ | 35/100 [01:00<01:48, 1.68s/it] 36%|█████████████████████████████▏ | 36/100 [01:01<01:47, 1.67s/it] 37%|█████████████████████████████▉ | 37/100 [01:03<01:45, 1.67s/it] 38%|██████████████████████████████▊ | 38/100 [01:05<01:43, 1.67s/it] 39%|███████████████████████████████▌ | 39/100 [01:06<01:41, 1.67s/it] 40%|████████████████████████████████▍ | 40/100 [01:08<01:39, 1.66s/it] 41%|█████████████████████████████████▏ | 41/100 [01:10<01:38, 1.66s/it] 42%|██████████████████████████████████ | 42/100 [01:11<01:35, 1.65s/it] 43%|██████████████████████████████████▊ | 43/100 [01:13<01:34, 1.66s/it] 44%|███████████████████████████████████▋ | 44/100 [01:15<01:32, 1.66s/it] 45%|████████████████████████████████████▍ | 45/100 [01:16<01:31, 1.66s/it][Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 1000 out of 1000 | elapsed: 1.3s finished
the number of miss classified sampels is 2984
46%|█████████████████████████████████████▎ | 46/100 [03:53<43:26, 48.27s/it] 47%|██████████████████████████████████████ | 47/100 [03:55<30:17, 34.29s/it] 48%|██████████████████████████████████████▉ | 48/100 [03:57<21:14, 24.50s/it] 49%|███████████████████████████████████████▋ | 49/100 [03:58<15:00, 17.65s/it] 50%|████████████████████████████████████████▌ | 50/100 [04:00<10:42, 12.86s/it] 51%|█████████████████████████████████████████▎ | 51/100 [04:02<07:45, 9.50s/it] 52%|██████████████████████████████████████████ | 52/100 [04:03<05:43, 7.16s/it] 53%|██████████████████████████████████████████▉ | 53/100 [04:05<04:19, 5.51s/it] 54%|███████████████████████████████████████████▋ | 54/100 [04:07<03:20, 4.36s/it] 55%|████████████████████████████████████████████▌ | 55/100 [04:08<02:39, 3.55s/it] 56%|█████████████████████████████████████████████▎ | 56/100 [04:10<02:11, 2.99s/it] 57%|██████████████████████████████████████████████▏ | 57/100 [04:12<01:51, 2.59s/it] 58%|██████████████████████████████████████████████▉ | 58/100 [04:13<01:37, 2.32s/it] 59%|███████████████████████████████████████████████▊ | 59/100 [04:15<01:27, 2.12s/it] 60%|████████████████████████████████████████████████▌ | 60/100 [04:17<01:19, 1.99s/it] 61%|█████████████████████████████████████████████████▍ | 61/100 [04:18<01:13, 1.90s/it] 62%|██████████████████████████████████████████████████▏ | 62/100 [04:20<01:09, 1.83s/it] 63%|███████████████████████████████████████████████████ | 63/100 [04:22<01:04, 1.75s/it] 64%|███████████████████████████████████████████████████▊ | 64/100 [04:23<01:01, 1.71s/it] 65%|████████████████████████████████████████████████████▋ | 65/100 [04:25<00:58, 1.68s/it] 66%|█████████████████████████████████████████████████████▍ | 66/100 [04:27<00:56, 1.66s/it] 67%|██████████████████████████████████████████████████████▎ | 67/100 [04:28<00:54, 1.64s/it] 68%|███████████████████████████████████████████████████████ | 68/100 [04:30<00:52, 1.63s/it] 69%|███████████████████████████████████████████████████████▉ | 69/100 [04:31<00:50, 1.62s/it] 70%|████████████████████████████████████████████████████████▋ | 70/100 [04:33<00:48, 1.61s/it] 71%|█████████████████████████████████████████████████████████▌ | 71/100 [04:34<00:46, 1.60s/it] 72%|██████████████████████████████████████████████████████████▎ | 72/100 [04:36<00:44, 1.60s/it] 73%|███████████████████████████████████████████████████████████▏ | 73/100 [04:38<00:43, 1.60s/it] 74%|███████████████████████████████████████████████████████████▉ | 74/100 [04:39<00:41, 1.60s/it] 75%|████████████████████████████████████████████████████████████▊ | 75/100 [04:41<00:40, 1.61s/it] 76%|█████████████████████████████████████████████████████████████▌ | 76/100 [04:43<00:38, 1.61s/it] 77%|██████████████████████████████████████████████████████████████▎ | 77/100 [04:44<00:36, 1.61s/it] 78%|███████████████████████████████████████████████████████████████▏ | 78/100 [04:46<00:35, 1.60s/it] 79%|███████████████████████████████████████████████████████████████▉ | 79/100 [04:47<00:33, 1.60s/it] 80%|████████████████████████████████████████████████████████████████▊ | 80/100 [04:49<00:31, 1.59s/it] 81%|█████████████████████████████████████████████████████████████████▌ | 81/100 [04:50<00:30, 1.60s/it] 82%|██████████████████████████████████████████████████████████████████▍ | 82/100 [04:52<00:28, 1.61s/it] 83%|███████████████████████████████████████████████████████████████████▏ | 83/100 [04:54<00:27, 1.61s/it] 84%|████████████████████████████████████████████████████████████████████ | 84/100 [04:55<00:25, 1.61s/it] 85%|████████████████████████████████████████████████████████████████████▊ | 85/100 [04:57<00:24, 1.61s/it] 86%|█████████████████████████████████████████████████████████████████████▋ | 86/100 [04:59<00:22, 1.60s/it] 87%|██████████████████████████████████████████████████████████████████████▍ | 87/100 [05:00<00:20, 1.59s/it] 88%|███████████████████████████████████████████████████████████████████████▎ | 88/100 [05:02<00:19, 1.59s/it] 89%|████████████████████████████████████████████████████████████████████████ | 89/100 [05:03<00:17, 1.59s/it] 90%|████████████████████████████████████████████████████████████████████████▉ | 90/100 [05:05<00:15, 1.59s/it] 91%|█████████████████████████████████████████████████████████████████████████▋ | 91/100 [05:06<00:14, 1.59s/it] 92%|██████████████████████████████████████████████████████████████████████████▌ | 92/100 [05:08<00:12, 1.59s/it] 93%|███████████████████████████████████████████████████████████████████████████▎ | 93/100 [05:10<00:11, 1.60s/it] 94%|████████████████████████████████████████████████████████████████████████████▏ | 94/100 [05:11<00:09, 1.60s/it] 95%|████████████████████████████████████████████████████████████████████████████▉ | 95/100 [05:13<00:08, 1.60s/it] 96%|█████████████████████████████████████████████████████████████████████████████▊ | 96/100 [05:14<00:06, 1.60s/it] 97%|██████████████████████████████████████████████████████████████████████████████▌ | 97/100 [05:16<00:04, 1.60s/it] 98%|███████████████████████████████████████████████████████████████████████████████▍ | 98/100 [05:18<00:03, 1.60s/it] 99%|████████████████████████████████████████████████████████████████████████████████▏| 99/100 [05:19<00:01, 1.60s/it] 100%|████████████████████████████████████████████████████████████████████████████████| 100/100 [05:21<00:00, 3.21s/it]
Global model loss: 3.510807255450716; global model accuracy: 0.5873957872390747 Round 10.
0%| | 0/100 [00:00<?, ?it/s] 1%|▊ | 1/100 [00:01<02:50, 1.72s/it] 2%|█▋ | 2/100 [00:03<02:49, 1.73s/it] 3%|██▍ | 3/100 [00:05<02:47, 1.72s/it] 4%|███▎ | 4/100 [00:06<02:45, 1.73s/it] 5%|████ | 5/100 [00:08<02:43, 1.72s/it] 6%|████▉ | 6/100 [00:10<02:40, 1.70s/it] 7%|█████▋ | 7/100 [00:11<02:38, 1.70s/it] 8%|██████▌ | 8/100 [00:13<02:35, 1.69s/it] 9%|███████▍ | 9/100 [00:15<02:34, 1.70s/it] 10%|████████ | 10/100 [00:17<02:33, 1.71s/it] 11%|████████▉ | 11/100 [00:18<02:32, 1.72s/it] 12%|█████████▋ | 12/100 [00:20<02:30, 1.71s/it] 13%|██████████▌ | 13/100 [00:22<02:29, 1.72s/it] 14%|███████████▎ | 14/100 [00:23<02:28, 1.72s/it] 15%|████████████▏ | 15/100 [00:25<02:25, 1.72s/it] 16%|████████████▉ | 16/100 [00:27<02:24, 1.72s/it] 17%|█████████████▊ | 17/100 [00:29<02:23, 1.73s/it] 18%|██████████████▌ | 18/100 [00:30<02:21, 1.73s/it] 19%|███████████████▍ | 19/100 [00:32<02:19, 1.72s/it] 20%|████████████████▏ | 20/100 [00:34<02:16, 1.71s/it] 21%|█████████████████ | 21/100 [00:35<02:14, 1.71s/it] 22%|█████████████████▊ | 22/100 [00:37<02:12, 1.70s/it] 23%|██████████████████▋ | 23/100 [00:39<02:10, 1.70s/it] 24%|███████████████████▍ | 24/100 [00:41<02:09, 1.70s/it] 25%|████████████████████▎ | 25/100 [00:42<02:07, 1.71s/it] 26%|█████████████████████ | 26/100 [00:44<02:05, 1.70s/it] 27%|█████████████████████▊ | 27/100 [00:46<02:04, 1.70s/it] 28%|██████████████████████▋ | 28/100 [00:47<02:02, 1.70s/it] 29%|███████████████████████▍ | 29/100 [00:49<02:00, 1.69s/it] 30%|████████████████████████▎ | 30/100 [00:51<01:59, 1.71s/it] 31%|█████████████████████████ | 31/100 [00:53<01:57, 1.71s/it] 32%|█████████████████████████▉ | 32/100 [00:54<01:56, 1.71s/it] 33%|██████████████████████████▋ | 33/100 [00:56<01:54, 1.71s/it] 34%|███████████████████████████▌ | 34/100 [00:58<01:52, 1.70s/it] 35%|████████████████████████████▎ | 35/100 [00:59<01:50, 1.69s/it] 36%|█████████████████████████████▏ | 36/100 [01:01<01:48, 1.69s/it] 37%|█████████████████████████████▉ | 37/100 [01:03<01:46, 1.70s/it] 38%|██████████████████████████████▊ | 38/100 [01:04<01:44, 1.69s/it] 39%|███████████████████████████████▌ | 39/100 [01:06<01:43, 1.70s/it] 40%|████████████████████████████████▍ | 40/100 [01:08<01:42, 1.70s/it] 41%|█████████████████████████████████▏ | 41/100 [01:09<01:40, 1.70s/it] 42%|██████████████████████████████████ | 42/100 [01:11<01:38, 1.69s/it] 43%|██████████████████████████████████▊ | 43/100 [01:13<01:36, 1.69s/it] 44%|███████████████████████████████████▋ | 44/100 [01:15<01:34, 1.69s/it] 45%|████████████████████████████████████▍ | 45/100 [01:16<01:33, 1.70s/it] 46%|█████████████████████████████████████▎ | 46/100 [01:18<01:31, 1.70s/it] 47%|██████████████████████████████████████ | 47/100 [01:20<01:30, 1.70s/it] 48%|██████████████████████████████████████▉ | 48/100 [01:21<01:28, 1.70s/it] 49%|███████████████████████████████████████▋ | 49/100 [01:23<01:26, 1.70s/it] 50%|████████████████████████████████████████▌ | 50/100 [01:25<01:24, 1.69s/it] 51%|█████████████████████████████████████████▎ | 51/100 [01:26<01:22, 1.69s/it] 52%|██████████████████████████████████████████ | 52/100 [01:28<01:20, 1.69s/it] 53%|██████████████████████████████████████████▉ | 53/100 [01:30<01:19, 1.69s/it] 54%|███████████████████████████████████████████▋ | 54/100 [01:32<01:18, 1.70s/it] 55%|████████████████████████████████████████████▌ | 55/100 [01:33<01:16, 1.69s/it] 56%|█████████████████████████████████████████████▎ | 56/100 [01:35<01:14, 1.70s/it] 57%|██████████████████████████████████████████████▏ | 57/100 [01:37<01:13, 1.71s/it] 58%|██████████████████████████████████████████████▉ | 58/100 [01:38<01:12, 1.72s/it] 59%|███████████████████████████████████████████████▊ | 59/100 [01:40<01:10, 1.72s/it] 60%|████████████████████████████████████████████████▌ | 60/100 [01:42<01:08, 1.72s/it] 61%|█████████████████████████████████████████████████▍ | 61/100 [01:44<01:06, 1.72s/it] 62%|██████████████████████████████████████████████████▏ | 62/100 [01:45<01:04, 1.71s/it] 63%|███████████████████████████████████████████████████ | 63/100 [01:47<01:03, 1.71s/it] 64%|███████████████████████████████████████████████████▊ | 64/100 [01:49<01:02, 1.74s/it] 65%|████████████████████████████████████████████████████▋ | 65/100 [01:50<01:00, 1.73s/it] 66%|█████████████████████████████████████████████████████▍ | 66/100 [01:52<00:58, 1.73s/it] 67%|██████████████████████████████████████████████████████▎ | 67/100 [01:54<00:56, 1.72s/it]
68%|███████████████████████████████████████████████████████ | 68/100 [01:56<00:55, 1.72s/it] 69%|███████████████████████████████████████████████████████▉ | 69/100 [01:57<00:53, 1.73s/it] 70%|████████████████████████████████████████████████████████▋ | 70/100 [01:59<00:51, 1.73s/it] 71%|█████████████████████████████████████████████████████████▌ | 71/100 [02:01<00:50, 1.73s/it] 72%|██████████████████████████████████████████████████████████▎ | 72/100 [02:03<00:48, 1.72s/it] 73%|███████████████████████████████████████████████████████████▏ | 73/100 [02:04<00:46, 1.73s/it] 74%|███████████████████████████████████████████████████████████▉ | 74/100 [02:06<00:44, 1.71s/it] 75%|████████████████████████████████████████████████████████████▊ | 75/100 [02:08<00:42, 1.72s/it] 76%|█████████████████████████████████████████████████████████████▌ | 76/100 [02:09<00:41, 1.72s/it][Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 1000 out of 1000 | elapsed: 1.2s finished
the number of miss classified sampels is 5261
77%|██████████████████████████████████████████████████████████████▎ | 77/100 [06:34<30:50, 80.47s/it] 78%|███████████████████████████████████████████████████████████████▏ | 78/100 [06:35<20:50, 56.85s/it] 79%|███████████████████████████████████████████████████████████████▉ | 79/100 [06:37<14:06, 40.30s/it] 80%|████████████████████████████████████████████████████████████████▊ | 80/100 [06:39<09:34, 28.72s/it] 81%|█████████████████████████████████████████████████████████████████▌ | 81/100 [06:40<06:31, 20.61s/it] 82%|██████████████████████████████████████████████████████████████████▍ | 82/100 [06:42<04:29, 14.95s/it] 83%|███████████████████████████████████████████████████████████████████▏ | 83/100 [06:44<03:06, 10.97s/it] 84%|████████████████████████████████████████████████████████████████████ | 84/100 [06:46<02:11, 8.19s/it] 85%|████████████████████████████████████████████████████████████████████▊ | 85/100 [06:47<01:33, 6.24s/it] 86%|█████████████████████████████████████████████████████████████████████▋ | 86/100 [06:49<01:08, 4.88s/it] 87%|██████████████████████████████████████████████████████████████████████▍ | 87/100 [06:51<00:50, 3.92s/it] 88%|███████████████████████████████████████████████████████████████████████▎ | 88/100 [06:52<00:39, 3.26s/it] 89%|████████████████████████████████████████████████████████████████████████ | 89/100 [06:54<00:30, 2.80s/it] 90%|████████████████████████████████████████████████████████████████████████▉ | 90/100 [06:56<00:24, 2.46s/it] 91%|█████████████████████████████████████████████████████████████████████████▋ | 91/100 [06:57<00:20, 2.25s/it] 92%|██████████████████████████████████████████████████████████████████████████▌ | 92/100 [06:59<00:16, 2.11s/it] 93%|███████████████████████████████████████████████████████████████████████████▎ | 93/100 [07:01<00:14, 2.02s/it] 94%|████████████████████████████████████████████████████████████████████████████▏ | 94/100 [07:03<00:11, 1.93s/it] 95%|████████████████████████████████████████████████████████████████████████████▉ | 95/100 [07:05<00:09, 1.87s/it] 96%|█████████████████████████████████████████████████████████████████████████████▊ | 96/100 [07:06<00:07, 1.82s/it] 97%|██████████████████████████████████████████████████████████████████████████████▌ | 97/100 [07:08<00:05, 1.78s/it] 98%|███████████████████████████████████████████████████████████████████████████████▍ | 98/100 [07:10<00:03, 1.75s/it] 99%|████████████████████████████████████████████████████████████████████████████████▏| 99/100 [07:11<00:01, 1.73s/it] 100%|████████████████████████████████████████████████████████████████████████████████| 100/100 [07:13<00:00, 4.34s/it]
Global model loss: 0.8507050739322711; global model accuracy: 0.7916574478149414
In [74]:
peers_selected
Out[74]:
[50]
In [75]:
FI_dic1
Out[75]:
{0: array([0.02392711, 0.02151661, 0.00187879, 0.00976734, 0.02580066, 0.00160656, 0.01195348, 0.00133958, 0.00250978, 0.00889068, 0.05876696, 0.00843687, 0.00386948, 0.01129838, 0.00221767, 0.03796889, 0.0323155 , 0.00240375, 0.03846723, 0.03199342, 0.00298984, 0.0034151 , 0.01042293, 0.0127904 , 0.01568604, 0.14358194, 0.00452941, 0.07049981, 0.00657889, 0.0022481 , 0.0793962 , 0.00643942, 0.00239138, 0.06767149, 0.03591957, 0.14058599, 0.02268127, 0.00775547, 0.02748803]), 1: array([0.02392711, 0.02151661, 0.00187879, 0.00976734, 0.02580066, 0.00160656, 0.01195348, 0.00133958, 0.00250978, 0.00889068, 0.05876696, 0.00843687, 0.00386948, 0.01129838, 0.00221767, 0.03796889, 0.0323155 , 0.00240375, 0.03846723, 0.03199342, 0.00298984, 0.0034151 , 0.01042293, 0.0127904 , 0.01568604, 0.14358194, 0.00452941, 0.07049981, 0.00657889, 0.0022481 , 0.0793962 , 0.00643942, 0.00239138, 0.06767149, 0.03591957, 0.14058599, 0.02268127, 0.00775547, 0.02748803]), 2: array([0.02392711, 0.02151661, 0.00187879, 0.00976734, 0.02580066, 0.00160656, 0.01195348, 0.00133958, 0.00250978, 0.00889068, 0.05876696, 0.00843687, 0.00386948, 0.01129838, 0.00221767, 0.03796889, 0.0323155 , 0.00240375, 0.03846723, 0.03199342, 0.00298984, 0.0034151 , 0.01042293, 0.0127904 , 0.01568604, 0.14358194, 0.00452941, 0.07049981, 0.00657889, 0.0022481 , 0.0793962 , 0.00643942, 0.00239138, 0.06767149, 0.03591957, 0.14058599, 0.02268127, 0.00775547, 0.02748803]), 3: array([0.02392711, 0.02151661, 0.00187879, 0.00976734, 0.02580066, 0.00160656, 0.01195348, 0.00133958, 0.00250978, 0.00889068, 0.05876696, 0.00843687, 0.00386948, 0.01129838, 0.00221767, 0.03796889, 0.0323155 , 0.00240375, 0.03846723, 0.03199342, 0.00298984, 0.0034151 , 0.01042293, 0.0127904 , 0.01568604, 0.14358194, 0.00452941, 0.07049981, 0.00657889, 0.0022481 , 0.0793962 , 0.00643942, 0.00239138, 0.06767149, 0.03591957, 0.14058599, 0.02268127, 0.00775547, 0.02748803]), 4: array([0.02392711, 0.02151661, 0.00187879, 0.00976734, 0.02580066, 0.00160656, 0.01195348, 0.00133958, 0.00250978, 0.00889068, 0.05876696, 0.00843687, 0.00386948, 0.01129838, 0.00221767, 0.03796889, 0.0323155 , 0.00240375, 0.03846723, 0.03199342, 0.00298984, 0.0034151 , 0.01042293, 0.0127904 , 0.01568604, 0.14358194, 0.00452941, 0.07049981, 0.00657889, 0.0022481 , 0.0793962 , 0.00643942, 0.00239138, 0.06767149, 0.03591957, 0.14058599, 0.02268127, 0.00775547, 0.02748803]), 5: array([0.02392711, 0.02151661, 0.00187879, 0.00976734, 0.02580066, 0.00160656, 0.01195348, 0.00133958, 0.00250978, 0.00889068, 0.05876696, 0.00843687, 0.00386948, 0.01129838, 0.00221767, 0.03796889, 0.0323155 , 0.00240375, 0.03846723, 0.03199342, 0.00298984, 0.0034151 , 0.01042293, 0.0127904 , 0.01568604, 0.14358194, 0.00452941, 0.07049981, 0.00657889, 0.0022481 , 0.0793962 , 0.00643942, 0.00239138, 0.06767149, 0.03591957, 0.14058599, 0.02268127, 0.00775547, 0.02748803]), 6: array([0.02392711, 0.02151661, 0.00187879, 0.00976734, 0.02580066, 0.00160656, 0.01195348, 0.00133958, 0.00250978, 0.00889068, 0.05876696, 0.00843687, 0.00386948, 0.01129838, 0.00221767, 0.03796889, 0.0323155 , 0.00240375, 0.03846723, 0.03199342, 0.00298984, 0.0034151 , 0.01042293, 0.0127904 , 0.01568604, 0.14358194, 0.00452941, 0.07049981, 0.00657889, 0.0022481 , 0.0793962 , 0.00643942, 0.00239138, 0.06767149, 0.03591957, 0.14058599, 0.02268127, 0.00775547, 0.02748803]), 7: array([0.02392711, 0.02151661, 0.00187879, 0.00976734, 0.02580066, 0.00160656, 0.01195348, 0.00133958, 0.00250978, 0.00889068, 0.05876696, 0.00843687, 0.00386948, 0.01129838, 0.00221767, 0.03796889, 0.0323155 , 0.00240375, 0.03846723, 0.03199342, 0.00298984, 0.0034151 , 0.01042293, 0.0127904 , 0.01568604, 0.14358194, 0.00452941, 0.07049981, 0.00657889, 0.0022481 , 0.0793962 , 0.00643942, 0.00239138, 0.06767149, 0.03591957, 0.14058599, 0.02268127, 0.00775547, 0.02748803]), 8: array([0.02392711, 0.02151661, 0.00187879, 0.00976734, 0.02580066, 0.00160656, 0.01195348, 0.00133958, 0.00250978, 0.00889068, 0.05876696, 0.00843687, 0.00386948, 0.01129838, 0.00221767, 0.03796889, 0.0323155 , 0.00240375, 0.03846723, 0.03199342, 0.00298984, 0.0034151 , 0.01042293, 0.0127904 , 0.01568604, 0.14358194, 0.00452941, 0.07049981, 0.00657889, 0.0022481 , 0.0793962 , 0.00643942, 0.00239138, 0.06767149, 0.03591957, 0.14058599, 0.02268127, 0.00775547, 0.02748803]), 9: array([0.02392711, 0.02151661, 0.00187879, 0.00976734, 0.02580066, 0.00160656, 0.01195348, 0.00133958, 0.00250978, 0.00889068, 0.05876696, 0.00843687, 0.00386948, 0.01129838, 0.00221767, 0.03796889, 0.0323155 , 0.00240375, 0.03846723, 0.03199342, 0.00298984, 0.0034151 , 0.01042293, 0.0127904 , 0.01568604, 0.14358194, 0.00452941, 0.07049981, 0.00657889, 0.0022481 , 0.0793962 , 0.00643942, 0.00239138, 0.06767149, 0.03591957, 0.14058599, 0.02268127, 0.00775547, 0.02748803])}
In [76]:
FI_dic1[9]
Out[76]:
array([0.02392711, 0.02151661, 0.00187879, 0.00976734, 0.02580066, 0.00160656, 0.01195348, 0.00133958, 0.00250978, 0.00889068, 0.05876696, 0.00843687, 0.00386948, 0.01129838, 0.00221767, 0.03796889, 0.0323155 , 0.00240375, 0.03846723, 0.03199342, 0.00298984, 0.0034151 , 0.01042293, 0.0127904 , 0.01568604, 0.14358194, 0.00452941, 0.07049981, 0.00657889, 0.0022481 , 0.0793962 , 0.00643942, 0.00239138, 0.06767149, 0.03591957, 0.14058599, 0.02268127, 0.00775547, 0.02748803])
In [77]:
FI_dic1[9]
Out[77]:
array([0.02392711, 0.02151661, 0.00187879, 0.00976734, 0.02580066, 0.00160656, 0.01195348, 0.00133958, 0.00250978, 0.00889068, 0.05876696, 0.00843687, 0.00386948, 0.01129838, 0.00221767, 0.03796889, 0.0323155 , 0.00240375, 0.03846723, 0.03199342, 0.00298984, 0.0034151 , 0.01042293, 0.0127904 , 0.01568604, 0.14358194, 0.00452941, 0.07049981, 0.00657889, 0.0022481 , 0.0793962 , 0.00643942, 0.00239138, 0.06767149, 0.03591957, 0.14058599, 0.02268127, 0.00775547, 0.02748803])
In [78]:
z=FI_dic1[9].max(axis = 0)
In [79]:
z
Out[79]:
0.14358194388079848
In [80]:
names = ['temp_hand','acceleration_16_x_hand', 'acceleration_16_y_hand','acceleration_16_z_hand','acceleration_6_x_hand', 'acceleration_6_y_hand','acceleration_6_z_hand','gyroscope_x_hand','gyroscope_y_hand', 'gyroscope_z_hand','magnetometer_x_hand','magnetometer_y_hand','magnetometer_z_hand', 'temp_chest','acceleration_16_x_chest','acceleration_16_y_chest','acceleration_16_z_chest','acceleration_6_x_chest', 'acceleration_6_y_chest','acceleration_6_z_chest','gyroscope_x_chest','gyroscope_y_chest','gyroscope_z_chest', 'magnetometer_x_chest','magnetometer_y_chest','magnetometer_z_chest','temp_ankle','acceleration_16_x_ankle', 'acceleration_16_y_ankle','acceleration_16_z_ankle','acceleration_6_x_ankle','acceleration_6_y_ankle', 'acceleration_6_z_ankle','gyroscope_x_ankle','gyroscope_y_ankle','gyroscope_z_ankle','magnetometer_x_ankle', 'magnetometer_y_ankle','magnetometer_z_ankle']
In [81]:
sort_index = np.argsort(FI_dic1[9])
In [82]:
for x in sort_index: print(names[x], ', ', FI_dic1[9][x])
gyroscope_x_hand , 0.001339578209529386 acceleration_6_y_hand , 0.001606558887748289 acceleration_16_y_hand , 0.0018787909132091745 acceleration_16_x_chest , 0.0022176706422468036 acceleration_16_z_ankle , 0.0022480971625748777 acceleration_6_z_ankle , 0.0023913839855701775 acceleration_6_x_chest , 0.002403746179862327 gyroscope_y_hand , 0.0025097802088887384 gyroscope_x_chest , 0.0029898374334842324 gyroscope_y_chest , 0.0034150962097039238 magnetometer_z_hand , 0.003869476254612703 temp_ankle , 0.004529408747298203 acceleration_6_y_ankle , 0.006439416466931228 acceleration_16_y_ankle , 0.006578886906287925 magnetometer_y_ankle , 0.007755468044512387 magnetometer_y_hand , 0.008436874146099699 gyroscope_z_hand , 0.00889068295023216 acceleration_16_z_hand , 0.009767340493272414 gyroscope_z_chest , 0.010422927880039217 temp_chest , 0.011298379110955319 acceleration_6_z_hand , 0.011953481433716266 magnetometer_x_chest , 0.012790397877654609 magnetometer_y_chest , 0.015686044856015324 acceleration_16_x_hand , 0.021516610095884795 magnetometer_x_ankle , 0.022681269695119605 temp_hand , 0.02392711201465511 acceleration_6_x_hand , 0.02580065752957123 magnetometer_z_ankle , 0.027488030896204812 acceleration_6_z_chest , 0.03199341773861375 acceleration_16_z_chest , 0.03231549779754809 gyroscope_y_ankle , 0.03591957265048234 acceleration_16_y_chest , 0.03796888825062477 acceleration_6_y_chest , 0.038467231682065194 magnetometer_x_hand , 0.05876695927124097 gyroscope_x_ankle , 0.06767148823051926 acceleration_16_x_ankle , 0.07049980505959738 acceleration_6_x_ankle , 0.07939620457238372 gyroscope_z_ankle , 0.1405859856342449 magnetometer_z_chest , 0.14358194388079848
In [83]:
len(FI_dic1[9])
Out[83]:
39
In [84]:
len(names)
Out[84]:
39
In [ ]: