70 lines
1.6 KiB
Plaintext
70 lines
1.6 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [],
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"source": [
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"from keras.applications.vgg19 import VGG19\n",
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"from keras.preprocessing import image\n",
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"from keras.applications.inception_v3 import preprocess_input\n",
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"from keras.models import Model\n",
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"import numpy as np\n",
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"\n",
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"# base_model = VGG19(weights='imagenet')\n",
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"# model = Model(inputs=base_model.input, outputs=base_model.get_layer('block4_pool').output)\n",
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"\n",
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"img_path = 'images/Achanthidium delmontii-!-Achnanthidium delmontii.JPG'\n",
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"img = image.load_img(img_path, target_size=(224, 224))\n",
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"x = image.img_to_array(img)\n",
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"x = np.expand_dims(x, axis=0)\n",
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"x = preprocess_input(x)\n",
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"\n",
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"# block4_pool_features = model.predict(x)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(-1.0, 1.0)"
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]
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},
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"execution_count": 15,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"x.min(), x.max()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.5.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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