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| def training():
list = os.listdir('C:/Users/rasam/Documents/ING5/PFE/classes/train')
number_class = len(list)
batch_size = 16
# build the MobileNet network
model = applications.mobilenet.MobileNet(input_shape=(224, 224, 3), alpha=1.0, depth_multiplier=1, dropout=1e-3, include_top=False, weights='imagenet', input_tensor=None, pooling=None)
# this is the augmentation configuration we will use for training
datagen = ImageDataGenerator(rescale=1. / 255)
generator = datagen.flow_from_directory(
'C:/Users/rasam/Documents/ING5/PFE/classes/train',
target_size=(224, 224),
batch_size=batch_size,
class_mode=None, # this means our generator will only yield batches of data, no labels #None
shuffle=False) # our data will be in order, so all first 1000 images will be cats, then 1000 dogs
# the predict_generator method returns the output of a model, given
# a generator that yields batches of numpy data
bottleneck_features_train = model.predict_generator(generator, (number_class * 1000)/batch_size)
# save the output as a Numpy array
np.save(open('bottleneck_features_train.npy', 'wb'), bottleneck_features_train)
generator = datagen.flow_from_directory(
'C:/Users/rasam/Documents/ING5/PFE/classes/validation',
target_size=(224, 224),
batch_size=batch_size,
class_mode=None, #class_mode=None,
shuffle=False)
print(generator.class_indices) #mapping from class names to class indices
#print(generator.classes) #array of class indices = labels
bottleneck_features_validation = model.predict_generator(generator, (number_class * 400)/batch_size)
np.save(open('bottleneck_features_validation.npy', 'wb'), bottleneck_features_validation)
train_data = np.load(open('bottleneck_features_train.npy', 'rb'))
# the features were saved in order, so recreating the labels is easy
validation_data = np.load(open('bottleneck_features_validation.npy', 'rb'))
for i in range(number_class-1):
if i==0:
train_labels = [i] * 1000 + [i+1] * 1000
validation_labels = [i] * 400 + [i+1] * 400
else:
train_labels = train_labels + [i+1] * 1000
validation_labels = validation_labels + [i+1] * 400
#train_labels = [0] * 1000 + [1] * 1000
#validation_labels = [0] * 400 + [1] * 400
train_labels = to_categorical(train_labels, num_classes=number_class)
validation_labels = to_categorical(validation_labels, num_classes=number_class)
top_model = Sequential()
top_model.add(GlobalAveragePooling2D(input_shape=train_data.shape[1:]))
top_model.add(Reshape((-1, 1, 1)))
top_model.add(Permute((3, 2, 1)))
top_model.add(Dropout(0.5))
top_model.add(Conv2D(number_class, (1, 1))) #top_model.add(Conv2D(number_class, (1, 1)))
top_model.add(Activation('softmax')) #top_model.add(Activation('softmax'))
top_model.add(Reshape(target_shape=(number_class, ))) #top_model.add(Reshape(target_shape=(number_class, ))) #model.add(Reshape(target_shape=(None, number_class)))
#plot_model(top_model, to_file='custom_last_layers_mobilenet.png')
top_model.summary()
"""
top_model.compile(optimizer='rmsprop', #optimizer='rmsprop'
loss='categorical_hinge', #loss='binary_crossentropy' in case of 2 classes #loss='categorical_crossentropy'
metrics=['accuracy'])
"""
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
top_model.compile(loss='categorical_crossentropy',
optimizer=sgd,
metrics=['accuracy'])
top_model.fit(train_data, train_labels,
epochs=25, #25
batch_size=batch_size,
validation_data=(validation_data, validation_labels))
top_model.save_weights('bottleneck_fc_model.h5') |
