Forked from fchollet/classifier_from_little_data_script_2.py
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June 11, 2016 10:17
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'''This script goes along the blog post | |
"Building powerful image classification models using very little data" | |
from blog.keras.io. | |
It uses data that can be downloaded at: | |
https://www.kaggle.com/c/dogs-vs-cats/data | |
In our setup, we: | |
- created a data/ folder | |
- created train/ and validation/ subfolders inside data/ | |
- created cats/ and dogs/ subfolders inside train/ and validation/ | |
- put the cat pictures index 0-999 in data/train/cats | |
- put the cat pictures index 1000-1400 in data/validation/cats | |
- put the dogs pictures index 12500-13499 in data/train/dogs | |
- put the dog pictures index 13500-13900 in data/validation/dogs | |
So that we have 1000 training examples for each class, and 400 validation examples for each class. | |
In summary, this is our directory structure: | |
``` | |
data/ | |
train/ | |
dogs/ | |
dog001.jpg | |
dog002.jpg | |
... | |
cats/ | |
cat001.jpg | |
cat002.jpg | |
... | |
validation/ | |
dogs/ | |
dog001.jpg | |
dog002.jpg | |
... | |
cats/ | |
cat001.jpg | |
cat002.jpg | |
... | |
``` | |
''' | |
import os | |
import h5py | |
import numpy as np | |
from keras.preprocessing.image import ImageDataGenerator | |
from keras.models import Sequential | |
from keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D | |
from keras.layers import Activation, Dropout, Flatten, Dense | |
# path to the model weights file. | |
weights_path = '../keras/examples/vgg16_weights.h5' | |
top_model_weights_path = 'bottleneck_fc_model.h5' | |
# dimensions of our images. | |
img_width, img_height = 150, 150 | |
train_data_dir = 'data/train' | |
validation_data_dir = 'data/validation' | |
nb_train_samples = 2000 | |
nb_validation_samples = 800 | |
nb_epoch = 50 | |
def save_bottlebeck_features(): | |
datagen = ImageDataGenerator(rescale=1./255) | |
# build the VGG16 network | |
model = Sequential() | |
model.add(ZeroPadding2D((1, 1), input_shape=(3, img_width, img_height))) | |
model.add(Convolution2D(64, 3, 3, activation='relu', name='conv1_1')) | |
model.add(ZeroPadding2D((1, 1))) | |
model.add(Convolution2D(64, 3, 3, activation='relu', name='conv1_2')) | |
model.add(MaxPooling2D((2, 2), strides=(2, 2))) | |
model.add(ZeroPadding2D((1, 1))) | |
model.add(Convolution2D(128, 3, 3, activation='relu', name='conv2_1')) | |
model.add(ZeroPadding2D((1, 1))) | |
model.add(Convolution2D(128, 3, 3, activation='relu', name='conv2_2')) | |
model.add(MaxPooling2D((2, 2), strides=(2, 2))) | |
model.add(ZeroPadding2D((1, 1))) | |
model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_1')) | |
model.add(ZeroPadding2D((1, 1))) | |
model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_2')) | |
model.add(ZeroPadding2D((1, 1))) | |
model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_3')) | |
model.add(MaxPooling2D((2, 2), strides=(2, 2))) | |
model.add(ZeroPadding2D((1, 1))) | |
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv4_1')) | |
model.add(ZeroPadding2D((1, 1))) | |
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv4_2')) | |
model.add(ZeroPadding2D((1, 1))) | |
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv4_3')) | |
model.add(MaxPooling2D((2, 2), strides=(2, 2))) | |
model.add(ZeroPadding2D((1, 1))) | |
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_1')) | |
model.add(ZeroPadding2D((1, 1))) | |
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_2')) | |
model.add(ZeroPadding2D((1, 1))) | |
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_3')) | |
model.add(MaxPooling2D((2, 2), strides=(2, 2))) | |
# load the weights of the VGG16 networks | |
# (trained on ImageNet, won the ILSVRC competition in 2014) | |
# note: when there is a complete match between your model definition | |
# and your weight savefile, you can simply call model.load_weights(filename) | |
assert os.path.exists(weights_path), 'Model weights not found (see "weights_path" variable in script).' | |
f = h5py.File(weights_path) | |
for k in range(f.attrs['nb_layers']): | |
if k >= len(model.layers): | |
# we don't look at the last (fully-connected) layers in the savefile | |
break | |
g = f['layer_{}'.format(k)] | |
weights = [g['param_{}'.format(p)] for p in range(g.attrs['nb_params'])] | |
model.layers[k].set_weights(weights) | |
f.close() | |
print('Model loaded.') | |
generator = datagen.flow_from_directory( | |
train_data_dir, | |
target_size=(img_width, img_height), | |
batch_size=32, | |
class_mode=None, | |
shuffle=False) | |
bottleneck_features_train = model.predict_generator(generator, nb_train_samples) | |
np.save(open('bottleneck_features_train.npy', 'w'), bottleneck_features_train) | |
generator = datagen.flow_from_directory( | |
validation_data_dir, | |
target_size=(img_width, img_height), | |
batch_size=32, | |
class_mode=None, | |
shuffle=False) | |
bottleneck_features_validation = model.predict_generator(generator, nb_validation_samples) | |
np.save(open('bottleneck_features_validation.npy', 'w'), bottleneck_features_validation) | |
def train_top_model(): | |
train_data = np.load(open('bottleneck_features_train.npy')) | |
train_labels = np.array([0] * (nb_train_samples / 2) + [1] * (nb_train_samples / 2)) | |
validation_data = np.load(open('bottleneck_features_validation.npy')) | |
validation_labels = np.array([0] * (nb_validation_samples / 2) + [1] * (nb_validation_samples / 2)) | |
model = Sequential() | |
model.add(Flatten(input_shape=train_data.shape[1:])) | |
model.add(Dense(256, activation='relu')) | |
model.add(Dropout(0.5)) | |
model.add(Dense(1, activation='sigmoid')) | |
model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy']) | |
model.fit(train_data, train_labels, | |
nb_epoch=nb_epoch, batch_size=32, | |
validation_data=(validation_data, validation_labels)) | |
model.save_weights(top_model_weights_path) | |
save_bottlebeck_features() | |
train_top_model() |
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