def test_sequential_fit_generator():
(x_train, y_train), (x_test, y_test) = _get_test_data()
def data_generator(train):
if train:
max_batch_index = len(x_train) // batch_size
else:
max_batch_index = len(x_test) // batch_size
i = 0
while 1:
if train:
yield (x_train[i * batch_size: (i + 1) * batch_size], y_train[i * batch_size: (i + 1) * batch_size])
else:
yield (x_test[i * batch_size: (i + 1) * batch_size], y_test[i * batch_size: (i + 1) * batch_size])
i += 1
i = i % max_batch_index
model = Sequential()
model.add(Dense(num_hidden, input_shape=(input_dim,)))
model.add(Activation('relu'))
model.add(Dense(num_class))
model.pop()
model.add(Dense(num_class))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
model.fit_generator(data_generator(True), 5, epochs)
model.fit_generator(data_generator(True), 5, epochs,
validation_data=(x_test, y_test))
model.fit_generator(data_generator(True), 5, epochs,
validation_data=data_generator(False),
validation_steps=3)
model.fit_generator(data_generator(True), 5, epochs, max_queue_size=2)
model.evaluate(x_train, y_train)
def build_model():
model = Sequential()
model.add(Embedding(input_dim=vocab_size,
output_dim=emb_dim,
input_length=maxlen,
weights=[embedding_weights]))
model.add(Dropout(0.2))
model.add(Lambda(lambda x: K.sum(x, axis=1), output_shape=(300,)))
model.add(Dropout(0.5))
model.add(Dense(512))
model.add(BatchNormalization())
model.add(PReLU())
model.add(Dropout(0.5))
model.add(RepeatVector(maxlen))
model.add(TimeDistributed(Dense(300)))
model.add(BatchNormalization())
model.add(PReLU())
model.add(Dropout(0.5))
model.add(TimeDistributed(Dense(vocab_size, activation='softmax')))
model.load_weights(weights_path)
for _ in range(6):
model.pop()
model.add(Dense(nb_labels, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
return model
def buildModel(self, modelPath):
layers = []
layers.append(ZeroPadding2D((1,1), input_shape=(3, 224, 224), name="input"))
layers.append(Conv2D(64, kernel_size=(3 , 3), activation='relu', name='conv1_1'))
layers.append(ZeroPadding2D((1, 1)))
layers.append(Conv2D(64, kernel_size=(3 , 3), activation='relu', name='conv1_2'))
layers.append(MaxPooling2D((2, 2), strides=(2, 2)))
layers.append(ZeroPadding2D((1, 1)))
layers.append(Conv2D(128, kernel_size=(3 , 3), activation='relu', name='conv2_1'))
layers.append(ZeroPadding2D((1, 1)))
layers.append(Conv2D(128, kernel_size=(3 , 3), activation='relu', name='conv2_2'))
layers.append(MaxPooling2D((2, 2), strides=(2, 2)))
layers.append(ZeroPadding2D((1, 1)))
layers.append(Conv2D(256, kernel_size=(3 , 3), activation='relu', name='conv3_1'))
layers.append(ZeroPadding2D((1, 1)))
layers.append(Conv2D(256, kernel_size=(3 , 3), activation='relu', name='conv3_2'))
layers.append(ZeroPadding2D((1, 1)))
layers.append(Conv2D(256, kernel_size=(3 , 3), activation='relu', name='conv3_3'))
layers.append(MaxPooling2D((2, 2), strides=(2, 2)))
layers.append(ZeroPadding2D((1, 1)))
layers.append(Conv2D(512, kernel_size=(3 , 3), activation='relu', name='conv4_1'))
layers.append(ZeroPadding2D((1, 1)))
layers.append(Conv2D(512, kernel_size=(3 , 3), activation='relu', name='conv4_2'))
layers.append(ZeroPadding2D((1, 1)))
layers.append(Conv2D(512, kernel_size=(3 , 3), activation='relu', name='conv4_3'))
layers.append(MaxPooling2D((2, 2), strides=(2, 2)))
layers.append(ZeroPadding2D((1, 1)))
layers.append(Conv2D(512, kernel_size=(3 , 3), activation='relu', name='conv5_1'))
layers.append(ZeroPadding2D((1, 1)))
layers.append(Conv2D(512, kernel_size=(3 , 3), activation='relu', name='conv5_2'))
layers.append(ZeroPadding2D((1, 1)))
layers.append(Conv2D(512, kernel_size=(3 , 3), activation='relu', name='conv5_3'))
layers.append(MaxPooling2D((2, 2), strides=(2, 2)))
layers.append(Flatten(name='flat'))
layers.append(Dense(4096, activation='relu', name='fc6'))
layers.append(Dropout(0.5, name='dopout0'))
layers.append(Dense(4096, activation='relu', name='fc7'))
layers.append(Dropout(0.5, name='dropout1'))
layers.append(Dense(1000, activation='softmax', name='softmax'))
model = Sequential()
for layer in layers:
model.add(layer)
while model.layers[-1].name != self.layerKey:
model.pop()
model.load_weights(modelPath, by_name=True)
self.model = TimeDistributed(model)
def test_sequential_pop():
model = Sequential()
model.add(Dense(num_hidden, input_dim=input_dim))
model.add(Dense(num_classes))
model.compile(loss='mse', optimizer='sgd')
x = np.random.random((batch_size, input_dim))
y = np.random.random((batch_size, num_classes))
model.fit(x, y, epochs=1)
model.pop()
assert len(model.layers) == 1
assert model.output_shape == (None, num_hidden)
model.compile(loss='mse', optimizer='sgd')
y = np.random.random((batch_size, num_hidden))
model.fit(x, y, epochs=1)
def test_sequential_fit_generator():
(X_train, y_train), (X_test, y_test) = _get_test_data()
def data_generator(train):
if train:
max_batch_index = len(X_train) // batch_size
else:
max_batch_index = len(X_test) // batch_size
i = 0
while 1:
if train:
yield (X_train[i * batch_size : (i + 1) * batch_size], y_train[i * batch_size : (i + 1) * batch_size])
else:
yield (X_test[i * batch_size : (i + 1) * batch_size], y_test[i * batch_size : (i + 1) * batch_size])
i += 1
i = i % max_batch_index
model = Sequential()
model.add(Dense(nb_hidden, input_shape=(input_dim,)))
model.add(Activation("relu"))
model.add(Dense(nb_class))
model.pop()
model.add(Dense(nb_class))
model.add(Activation("softmax"))
model.compile(loss="categorical_crossentropy", optimizer="rmsprop")
model.fit_generator(data_generator(True), len(X_train), nb_epoch)
model.fit_generator(data_generator(True), len(X_train), nb_epoch, validation_data=(X_test, y_test))
model.fit_generator(
data_generator(True),
len(X_train),
nb_epoch,
validation_data=data_generator(False),
nb_val_samples=batch_size * 3,
)
model.fit_generator(data_generator(True), len(X_train), nb_epoch, max_q_size=2)
loss = model.evaluate(X_train, y_train)
