andrewssobral · June 9, 2016 12:52 · Jun 6, 2016
diff --git a/classifier_from_little_data_script_2.py b/classifier_from_little_data_script_2.py
@@ -0,0 +1,162 @@
+'''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()