191 lines
4.8 KiB
Plaintext
191 lines
4.8 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Importing libraries\n",
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"from keras.models import Sequential\n",
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"from keras.layers import Conv2D\n",
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"from keras.layers import MaxPooling2D\n",
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"from keras.layers import Flatten\n",
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"from keras.layers import Dense"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Initialising the CNN\n",
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"classifier = Sequential()"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Step 1 - Convolution\n",
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"classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Step 2 - Pooling\n",
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"classifier.add(MaxPooling2D(pool_size = (2, 2)))"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Adding a second convolutional layer\n",
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"classifier.add(Conv2D(32, (3, 3), activation = 'relu'))\n",
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"classifier.add(MaxPooling2D(pool_size = (2, 2)))"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Step 3 - Flattening\n",
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"classifier.add(Flatten())"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Step 4 - Full connection\n",
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"classifier.add(Dense(units = 128, activation = 'relu'))\n",
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"classifier.add(Dense(units = 1, activation = 'sigmoid'))"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Compiling the CNN\n",
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"classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Partie 2 - CNN"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from keras.preprocessing.image import ImageDataGenerator"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"train_datagen = ImageDataGenerator(rescale = 1./255,\n",
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" shear_range = 0.2,\n",
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" zoom_range = 0.2,\n",
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" horizontal_flip = True)\n",
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"\n",
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"test_datagen = ImageDataGenerator(rescale = 1./255)\n",
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"\n",
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"training_set = train_datagen.flow_from_directory('dataset/training_set',\n",
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" target_size = (64, 64),\n",
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" batch_size = 32,\n",
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" class_mode = 'binary')\n",
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"\n",
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"test_set = test_datagen.flow_from_directory('dataset/test_set',\n",
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" target_size = (64, 64),\n",
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" batch_size = 32,\n",
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" class_mode = 'binary')"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"classifier.fit_generator(training_set,\n",
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" steps_per_epoch = 8000,\n",
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" epochs = 25,\n",
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" validation_data = test_set,\n",
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" validation_steps = 2000)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Partie 3 - Prédictions"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from keras.preprocessing import image\n",
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"test_image = image.load_img('dataset/single_prediction/cat_or_dog_1.jpg', target_size = (64, 64))\n",
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"test_image = image.img_to_array(test_image)\n",
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"test_image = np.expand_dims(test_image, axis = 0)\n",
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"result = classifier.predict(test_image)\n",
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"training_set.class_indices\n",
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"if result[0][0] == 1:\n",
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" prediction = 'dog'\n",
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"else:\n",
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" prediction = 'cat'"
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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": "3.6.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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