def get_model(shape, dropout=0.5, path=None):
print('building neural network')
model=Sequential()
model.add(Convolution2D(512, 3, 3, border_mode='same', input_shape=shape))
model.add(Activation('relu'))
model.add(Convolution2D(512, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(SpatialDropout2D(dropout))
model.add(Flatten())#input_shape=shape))
# model.add(Dense(4096))
# model.add(Activation('relu'))
# model.add(Dropout(0.5))
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
#model.add(Activation('linear'))
return model
python类SpatialDropout2D()的实例源码
def test_tiny_conv_dropout_random(self):
np.random.seed(1988)
num_samples = 1
input_dim = 8
input_shape = (input_dim, input_dim, 3)
num_kernels = 2
kernel_height = 5
kernel_width = 5
hidden_dim = 4
# Define a model
model = Sequential()
model.add(Conv2D(input_shape = input_shape,
filters = num_kernels, kernel_size=(kernel_height, kernel_width)))
model.add(SpatialDropout2D(0.5))
model.add(Flatten())
model.add(Dense(hidden_dim))
# Set some random weights
model.set_weights([np.random.rand(*w.shape) for w in model.get_weights()])
# Get the coreml model
self._test_keras_model(model)
def test_dropout():
layer_test(core.Dropout,
kwargs={'p': 0.5},
input_shape=(3, 2))
layer_test(core.SpatialDropout1D,
kwargs={'p': 0.5},
input_shape=(2, 3, 4))
layer_test(core.SpatialDropout2D,
kwargs={'p': 0.5},
input_shape=(2, 3, 4, 5))
layer_test(core.SpatialDropout3D,
kwargs={'p': 0.5},
input_shape=(2, 3, 4, 5, 6))
def test_dropout():
layer_test(core.Dropout,
kwargs={'p': 0.5},
input_shape=(3, 2))
layer_test(core.Dropout,
kwargs={'p': 0.5, 'noise_shape': [3, 1]},
input_shape=(3, 2))
layer_test(core.SpatialDropout1D,
kwargs={'p': 0.5},
input_shape=(2, 3, 4))
layer_test(core.SpatialDropout2D,
kwargs={'p': 0.5},
input_shape=(2, 3, 4, 5))
layer_test(core.SpatialDropout3D,
kwargs={'p': 0.5},
input_shape=(2, 3, 4, 5, 6))
def test_dropout():
layer_test(core.Dropout,
kwargs={'p': 0.5},
input_shape=(3, 2))
layer_test(core.SpatialDropout1D,
kwargs={'p': 0.5},
input_shape=(2, 3, 4))
layer_test(core.SpatialDropout2D,
kwargs={'p': 0.5},
input_shape=(2, 3, 4, 5))
layer_test(core.SpatialDropout3D,
kwargs={'p': 0.5},
input_shape=(2, 3, 4, 5, 6))
def bottleneck(inp, output, internal_scale=4, asymmetric=0, dilated=0, downsample=False, dropout_rate=0.1):
# main branch
internal = output // internal_scale
encoder = inp
# 1x1
input_stride = 2 if downsample else 1 # the 1st 1x1 projection is replaced with a 2x2 convolution when downsampling
encoder = Conv2D(internal, (input_stride, input_stride),
# padding='same',
strides=(input_stride, input_stride), use_bias=False)(encoder)
# Batch normalization + PReLU
encoder = BatchNormalization(momentum=0.1)(encoder) # enet uses momentum of 0.1, keras default is 0.99
encoder = PReLU(shared_axes=[1, 2])(encoder)
# conv
if not asymmetric and not dilated:
encoder = Conv2D(internal, (3, 3), padding='same')(encoder)
elif asymmetric:
encoder = Conv2D(internal, (1, asymmetric), padding='same', use_bias=False)(encoder)
encoder = Conv2D(internal, (asymmetric, 1), padding='same')(encoder)
elif dilated:
encoder = Conv2D(internal, (3, 3), dilation_rate=(dilated, dilated), padding='same')(encoder)
else:
raise(Exception('You shouldn\'t be here'))
encoder = BatchNormalization(momentum=0.1)(encoder) # enet uses momentum of 0.1, keras default is 0.99
encoder = PReLU(shared_axes=[1, 2])(encoder)
# 1x1
encoder = Conv2D(output, (1, 1), use_bias=False)(encoder)
encoder = BatchNormalization(momentum=0.1)(encoder) # enet uses momentum of 0.1, keras default is 0.99
encoder = SpatialDropout2D(dropout_rate)(encoder)
other = inp
# other branch
if downsample:
other = MaxPooling2D()(other)
other = Permute((1, 3, 2))(other)
pad_feature_maps = output - inp.get_shape().as_list()[3]
tb_pad = (0, 0)
lr_pad = (0, pad_feature_maps)
other = ZeroPadding2D(padding=(tb_pad, lr_pad))(other)
other = Permute((1, 3, 2))(other)
encoder = add([encoder, other])
encoder = PReLU(shared_axes=[1, 2])(encoder)
return encoder
def bottleneck(inp, output, internal_scale=4, asymmetric=0, dilated=0, downsample=False, dropout_rate=0.1):
# main branch
internal = output // internal_scale
encoder = inp
# 1x1
input_stride = 2 if downsample else 1 # the 1st 1x1 projection is replaced with a 2x2 convolution when downsampling
encoder = Conv2D(internal, (input_stride, input_stride),
# padding='same',
strides=(input_stride, input_stride), use_bias=False)(encoder)
# Batch normalization + PReLU
encoder = BatchNormalization(momentum=0.1)(encoder) # enet_unpooling uses momentum of 0.1, keras default is 0.99
encoder = PReLU(shared_axes=[1, 2])(encoder)
# conv
if not asymmetric and not dilated:
encoder = Conv2D(internal, (3, 3), padding='same')(encoder)
elif asymmetric:
encoder = Conv2D(internal, (1, asymmetric), padding='same', use_bias=False)(encoder)
encoder = Conv2D(internal, (asymmetric, 1), padding='same')(encoder)
elif dilated:
encoder = Conv2D(internal, (3, 3), dilation_rate=(dilated, dilated), padding='same')(encoder)
else:
raise(Exception('You shouldn\'t be here'))
encoder = BatchNormalization(momentum=0.1)(encoder) # enet_unpooling uses momentum of 0.1, keras default is 0.99
encoder = PReLU(shared_axes=[1, 2])(encoder)
# 1x1
encoder = Conv2D(output, (1, 1), use_bias=False)(encoder)
encoder = BatchNormalization(momentum=0.1)(encoder) # enet_unpooling uses momentum of 0.1, keras default is 0.99
encoder = SpatialDropout2D(dropout_rate)(encoder)
other = inp
# other branch
if downsample:
other, indices = MaxPoolingWithArgmax2D()(other)
other = Permute((1, 3, 2))(other)
pad_feature_maps = output - inp.get_shape().as_list()[3]
tb_pad = (0, 0)
lr_pad = (0, pad_feature_maps)
other = ZeroPadding2D(padding=(tb_pad, lr_pad))(other)
other = Permute((1, 3, 2))(other)
encoder = add([encoder, other])
encoder = PReLU(shared_axes=[1, 2])(encoder)
if downsample:
return encoder, indices
else:
return encoder
