def seq3DCNN(n_flow=4, seq_len=3, map_height=32, map_width=32):
model=Sequential()
# model.add(ZeroPadding3D(padding=(0, 1, 1), input_shape=(n_flow, seq_len, map_height, map_width)))
# model.add(Convolution3D(64, 2, 3, 3, border_mode='valid'))
model.add(Convolution3D(64, 2, 3, 3, border_mode='same', input_shape=(n_flow, seq_len, map_height, map_width)))
model.add(Activation('relu'))
model.add(Convolution3D(128, 2, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution3D(64, 2, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(ZeroPadding3D(padding=(0, 1, 1)))
model.add(Convolution3D(n_flow, seq_len, 3, 3, border_mode='valid'))
# model.add(Convolution3D(n_flow, seq_len-2, 3, 3, border_mode='same'))
model.add(Activation('tanh'))
return model
python类ZeroPadding3D()的实例源码
def test_zero_padding_3d():
nb_samples = 2
stack_size = 2
input_len_dim1 = 4
input_len_dim2 = 5
input_len_dim3 = 3
input = np.ones((nb_samples,
input_len_dim1, input_len_dim2, input_len_dim3,
stack_size))
# basic test
layer_test(convolutional.ZeroPadding3D,
kwargs={'padding': (2, 2, 2)},
input_shape=input.shape)
# correctness test
layer = convolutional.ZeroPadding3D(padding=(2, 2, 2))
layer.build(input.shape)
output = layer(K.variable(input))
np_output = K.eval(output)
for offset in [0, 1, -1, -2]:
assert_allclose(np_output[:, offset, :, :, :], 0.)
assert_allclose(np_output[:, :, offset, :, :], 0.)
assert_allclose(np_output[:, :, :, offset, :], 0.)
assert_allclose(np_output[:, 2:-2, 2:-2, 2:-2, :], 1.)
layer.get_config()
def test_zero_padding_3d():
nb_samples = 2
stack_size = 2
input_len_dim1 = 4
input_len_dim2 = 5
input_len_dim3 = 3
input = np.ones((nb_samples,
input_len_dim1, input_len_dim2, input_len_dim3,
stack_size))
# basic test
layer_test(convolutional.ZeroPadding3D,
kwargs={'padding': (2, 2, 2)},
input_shape=input.shape)
# correctness test
layer = convolutional.ZeroPadding3D(padding=(2, 2, 2))
layer.build(input.shape)
output = layer(K.variable(input))
np_output = K.eval(output)
for offset in [0, 1, -1, -2]:
assert_allclose(np_output[:, offset, :, :, :], 0.)
assert_allclose(np_output[:, :, offset, :, :], 0.)
assert_allclose(np_output[:, :, :, offset, :], 0.)
assert_allclose(np_output[:, 2:-2, 2:-2, 2:-2, :], 1.)
layer.get_config()
def test_zero_padding_3d():
nb_samples = 2
stack_size = 2
input_len_dim1 = 4
input_len_dim2 = 5
input_len_dim3 = 3
input = np.ones((nb_samples,
input_len_dim1, input_len_dim2, input_len_dim3,
stack_size))
# basic test
layer_test(convolutional.ZeroPadding3D,
kwargs={'padding': (2, 2, 2)},
input_shape=input.shape)
# correctness test
layer = convolutional.ZeroPadding3D(padding=(2, 2, 2))
layer.build(input.shape)
output = layer(K.variable(input))
np_output = K.eval(output)
for offset in [0, 1, -1, -2]:
assert_allclose(np_output[:, offset, :, :, :], 0.)
assert_allclose(np_output[:, :, offset, :, :], 0.)
assert_allclose(np_output[:, :, :, offset, :], 0.)
assert_allclose(np_output[:, 2:-2, 2:-2, 2:-2, :], 1.)
layer.get_config()
def get_model(summary=False, backend='tf'):
""" Return the Keras model of the network
"""
model = Sequential()
if backend == 'tf':
input_shape=(16, 112, 112, 3) # l, h, w, c
else:
input_shape=(3, 16, 112, 112) # c, l, h, w
model.add(Convolution3D(64, 3, 3, 3, activation='relu',
border_mode='same', name='conv1',
input_shape=input_shape))
model.add(MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2),
border_mode='valid', name='pool1'))
# 2nd layer group
model.add(Convolution3D(128, 3, 3, 3, activation='relu',
border_mode='same', name='conv2'))
model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2),
border_mode='valid', name='pool2'))
# 3rd layer group
model.add(Convolution3D(256, 3, 3, 3, activation='relu',
border_mode='same', name='conv3a'))
model.add(Convolution3D(256, 3, 3, 3, activation='relu',
border_mode='same', name='conv3b'))
model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2),
border_mode='valid', name='pool3'))
# 4th layer group
model.add(Convolution3D(512, 3, 3, 3, activation='relu',
border_mode='same', name='conv4a'))
model.add(Convolution3D(512, 3, 3, 3, activation='relu',
border_mode='same', name='conv4b'))
model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2),
border_mode='valid', name='pool4'))
# 5th layer group
model.add(Convolution3D(512, 3, 3, 3, activation='relu',
border_mode='same', name='conv5a'))
model.add(Convolution3D(512, 3, 3, 3, activation='relu',
border_mode='same', name='conv5b'))
model.add(ZeroPadding3D(padding=((0, 0), (0, 1), (0, 1)), name='zeropad5'))
model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2),
border_mode='valid', name='pool5'))
model.add(Flatten())
# FC layers group
model.add(Dense(4096, activation='relu', name='fc6'))
model.add(Dropout(.5))
model.add(Dense(4096, activation='relu', name='fc7'))
model.add(Dropout(.5))
model.add(Dense(487, activation='softmax', name='fc8'))
if summary:
print(model.summary())
return model
