def unet_model_xd3_2_6l_grid(nb_filter=48, dim=5, clen=3 , img_rows=224, img_cols=224 ): # NOTE that this procedure is/should be used with img_rows & img_cols as None
# aiming for architecture similar to the http://cs231n.stanford.edu/reports2016/317_Report.pdf
# Our model is six layers deep, consisting of a series of three CONV-RELU-POOL layyers (with 32, 32, and 64 3x3 filters), a CONV-RELU layer (with 128 3x3 filters), three UPSCALE-CONV-RELU lay- ers (with 64, 32, and 32 3x3 filters), and a final 1x1 CONV- SIGMOID layer to output pixel-level predictions. Its struc- ture resembles Figure 2, though with the number of pixels, filters, and levels as described here
## 3D CNN version of a previously developed unet_model_xd_6j
zconv = clen
inputs = Input((1, dim, img_rows, img_cols))
conv1 = Convolution3D(nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(inputs)
conv1 = Convolution3D(nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(conv1)
pool1 = MaxPooling3D(pool_size=(2, 2, 2))(conv1)
conv2 = Convolution3D(2*nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(pool1)
conv2 = Convolution3D(2*nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(conv2)
pool2 = MaxPooling3D(pool_size=(2, 2, 2))(conv2)
conv4 = Convolution3D(4*nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(pool2)
conv4 = Convolution3D(4*nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(conv4)
up6 = merge([UpSampling3D(size=(2, 2, 2))(conv4), conv2], mode='concat', concat_axis=1)
conv6 = Convolution3D(2*nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(up6)
conv6 = Convolution3D(2*nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(conv6)
up7 = merge([UpSampling3D(size=(2, 2, 2))(conv6), conv1], mode='concat', concat_axis=1) # original - only works for even dim
conv7 = Convolution3D(nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(up7)
conv7 = Convolution3D(nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(conv7)
pool11 = MaxPooling3D(pool_size=(2, 1, 1))(conv7)
conv12 = Convolution3D(2*nb_filter, zconv, 1, 1, activation='relu', border_mode='same')(pool11)
conv12 = Convolution3D(2*nb_filter, zconv, 1, 1, activation='relu', border_mode='same')(conv12)
pool12 = MaxPooling3D(pool_size=(2, 1, 1))(conv12)
conv13 = Convolution3D(2*nb_filter, zconv, 1, 1, activation='relu', border_mode='same')(pool12)
conv13 = Convolution3D(2*nb_filter, zconv, 1, 1, activation='relu', border_mode='same')(conv13)
pool13 = MaxPooling3D(pool_size=(2, 1, 1))(conv13)
if (dim < 16):
conv8 = Convolution3D(1, 1, 1, 1, activation='sigmoid')(pool13)
else: # need one extra layer to get to 1D x 2D mask ...
conv14 = Convolution3D(2*nb_filter, zconv, 1, 1, activation='relu', border_mode='same')(pool13)
conv14 = Convolution3D(2*nb_filter, zconv, 1, 1, activation='relu', border_mode='same')(conv14)
pool14 = MaxPooling3D(pool_size=(2, 1, 1))(conv14)
conv8 = Convolution3D(1, 1, 1, 1, activation='sigmoid')(pool14)
model = Model(input=inputs, output=conv8)
model.compile(optimizer=Adam(lr=1e-4), loss=dice_coef_loss, metrics=[dice_coef])
#model.compile(optimizer=Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0), loss=dice_coef_loss, metrics=[dice_coef])
return model
python类MaxPooling3D()的实例源码
def unet_model_xd3_2_6l_grid(nb_filter=48, dim=5, clen=3 , img_rows=224, img_cols=224 ): # NOTE that this procedure is/should be used with img_rows & img_cols as None
# aiming for architecture similar to the http://cs231n.stanford.edu/reports2016/317_Report.pdf
# Our model is six layers deep, consisting of a series of three CONV-RELU-POOL layyers (with 32, 32, and 64 3x3 filters), a CONV-RELU layer (with 128 3x3 filters), three UPSCALE-CONV-RELU lay- ers (with 64, 32, and 32 3x3 filters), and a final 1x1 CONV- SIGMOID layer to output pixel-level predictions. Its struc- ture resembles Figure 2, though with the number of pixels, filters, and levels as described here
## 3D CNN version of a previously developed unet_model_xd_6j
zconv = clen
inputs = Input((1, dim, img_rows, img_cols))
conv1 = Convolution3D(nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(inputs)
conv1 = Convolution3D(nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(conv1)
pool1 = MaxPooling3D(pool_size=(2, 2, 2))(conv1)
conv2 = Convolution3D(2*nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(pool1)
conv2 = Convolution3D(2*nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(conv2)
pool2 = MaxPooling3D(pool_size=(2, 2, 2))(conv2)
conv4 = Convolution3D(4*nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(pool2)
conv4 = Convolution3D(4*nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(conv4)
up6 = merge([UpSampling3D(size=(2, 2, 2))(conv4), conv2], mode='concat', concat_axis=1)
conv6 = Convolution3D(2*nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(up6)
conv6 = Convolution3D(2*nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(conv6)
up7 = merge([UpSampling3D(size=(2, 2, 2))(conv6), conv1], mode='concat', concat_axis=1) # original - only works for even dim
conv7 = Convolution3D(nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(up7)
conv7 = Convolution3D(nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(conv7)
pool11 = MaxPooling3D(pool_size=(2, 1, 1))(conv7)
conv12 = Convolution3D(2*nb_filter, zconv, 1, 1, activation='relu', border_mode='same')(pool11)
conv12 = Convolution3D(2*nb_filter, zconv, 1, 1, activation='relu', border_mode='same')(conv12)
pool12 = MaxPooling3D(pool_size=(2, 1, 1))(conv12)
conv13 = Convolution3D(2*nb_filter, zconv, 1, 1, activation='relu', border_mode='same')(pool12)
conv13 = Convolution3D(2*nb_filter, zconv, 1, 1, activation='relu', border_mode='same')(conv13)
pool13 = MaxPooling3D(pool_size=(2, 1, 1))(conv13)
if (dim < 16):
conv8 = Convolution3D(1, 1, 1, 1, activation='sigmoid')(pool13)
else: # need one extra layer to get to 1D x 2D mask ...
conv14 = Convolution3D(2*nb_filter, zconv, 1, 1, activation='relu', border_mode='same')(pool13)
conv14 = Convolution3D(2*nb_filter, zconv, 1, 1, activation='relu', border_mode='same')(conv14)
pool14 = MaxPooling3D(pool_size=(2, 1, 1))(conv14)
conv8 = Convolution3D(1, 1, 1, 1, activation='sigmoid')(pool14)
model = Model(input=inputs, output=conv8)
model.compile(optimizer=Adam(lr=1e-4), loss=dice_coef_loss, metrics=[dice_coef])
#model.compile(optimizer=Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0), loss=dice_coef_loss, metrics=[dice_coef])
return model
def unet_model_xd3_2_6l_grid(nb_filter=48, dim=5, clen=3 , img_rows=224, img_cols=224 ): # NOTE that this procedure is/should be used with img_rows & img_cols as None
# aiming for architecture similar to the http://cs231n.stanford.edu/reports2016/317_Report.pdf
# Our model is six layers deep, consisting of a series of three CONV-RELU-POOL layyers (with 32, 32, and 64 3x3 filters), a CONV-RELU layer (with 128 3x3 filters), three UPSCALE-CONV-RELU lay- ers (with 64, 32, and 32 3x3 filters), and a final 1x1 CONV- SIGMOID layer to output pixel-level predictions. Its struc- ture resembles Figure 2, though with the number of pixels, filters, and levels as described here
## 3D CNN version of a previously developed unet_model_xd_6j
zconv = clen
inputs = Input((1, dim, img_rows, img_cols))
conv1 = Convolution3D(nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(inputs)
conv1 = Convolution3D(nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(conv1)
pool1 = MaxPooling3D(pool_size=(2, 2, 2))(conv1)
conv2 = Convolution3D(2*nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(pool1)
conv2 = Convolution3D(2*nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(conv2)
pool2 = MaxPooling3D(pool_size=(2, 2, 2))(conv2)
conv4 = Convolution3D(4*nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(pool2)
conv4 = Convolution3D(4*nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(conv4)
up6 = merge([UpSampling3D(size=(2, 2, 2))(conv4), conv2], mode='concat', concat_axis=1)
conv6 = Convolution3D(2*nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(up6)
conv6 = Convolution3D(2*nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(conv6)
up7 = merge([UpSampling3D(size=(2, 2, 2))(conv6), conv1], mode='concat', concat_axis=1) # original - only works for even dim
conv7 = Convolution3D(nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(up7)
conv7 = Convolution3D(nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(conv7)
pool11 = MaxPooling3D(pool_size=(2, 1, 1))(conv7)
conv12 = Convolution3D(2*nb_filter, zconv, 1, 1, activation='relu', border_mode='same')(pool11)
conv12 = Convolution3D(2*nb_filter, zconv, 1, 1, activation='relu', border_mode='same')(conv12)
pool12 = MaxPooling3D(pool_size=(2, 1, 1))(conv12)
conv13 = Convolution3D(2*nb_filter, zconv, 1, 1, activation='relu', border_mode='same')(pool12)
conv13 = Convolution3D(2*nb_filter, zconv, 1, 1, activation='relu', border_mode='same')(conv13)
pool13 = MaxPooling3D(pool_size=(2, 1, 1))(conv13)
if (dim < 16):
conv8 = Convolution3D(1, 1, 1, 1, activation='sigmoid')(pool13)
else: # need one extra layer to get to 1D x 2D mask ...
conv14 = Convolution3D(2*nb_filter, zconv, 1, 1, activation='relu', border_mode='same')(pool13)
conv14 = Convolution3D(2*nb_filter, zconv, 1, 1, activation='relu', border_mode='same')(conv14)
pool14 = MaxPooling3D(pool_size=(2, 1, 1))(conv14)
conv8 = Convolution3D(1, 1, 1, 1, activation='sigmoid')(pool14)
model = Model(input=inputs, output=conv8)
model.compile(optimizer=Adam(lr=1e-4), loss=dice_coef_loss, metrics=[dice_coef])
#model.compile(optimizer=Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0), loss=dice_coef_loss, metrics=[dice_coef])
return model
def build(video_shape, audio_spectrogram_size):
model = Sequential()
model.add(ZeroPadding3D(padding=(1, 2, 2), name='zero1', input_shape=video_shape))
model.add(Convolution3D(32, (3, 5, 5), strides=(1, 2, 2), kernel_initializer='he_normal', name='conv1'))
model.add(BatchNormalization())
model.add(LeakyReLU())
model.add(MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2), name='max1'))
model.add(Dropout(0.25))
model.add(ZeroPadding3D(padding=(1, 2, 2), name='zero2'))
model.add(Convolution3D(64, (3, 5, 5), strides=(1, 1, 1), kernel_initializer='he_normal', name='conv2'))
model.add(BatchNormalization())
model.add(LeakyReLU())
model.add(MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2), name='max2'))
model.add(Dropout(0.25))
model.add(ZeroPadding3D(padding=(1, 1, 1), name='zero3'))
model.add(Convolution3D(128, (3, 3, 3), strides=(1, 1, 1), kernel_initializer='he_normal', name='conv3'))
model.add(BatchNormalization())
model.add(LeakyReLU())
model.add(MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2), name='max3'))
model.add(Dropout(0.25))
model.add(TimeDistributed(Flatten(), name='time'))
model.add(Dense(1024, kernel_initializer='he_normal', name='dense1'))
model.add(BatchNormalization())
model.add(LeakyReLU())
model.add(Dropout(0.25))
model.add(Dense(1024, kernel_initializer='he_normal', name='dense2'))
model.add(BatchNormalization())
model.add(LeakyReLU())
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(2048, kernel_initializer='he_normal', name='dense3'))
model.add(BatchNormalization())
model.add(LeakyReLU())
model.add(Dropout(0.25))
model.add(Dense(2048, kernel_initializer='he_normal', name='dense4'))
model.add(BatchNormalization())
model.add(LeakyReLU())
model.add(Dropout(0.25))
model.add(Dense(audio_spectrogram_size, name='output'))
model.summary()
return VideoToSpeechNet(model)
def ecog_3d_model(channels=None, weights=None):
input_tensor = Input(shape=(1,8,8, 1000))
# Block 1
x = AveragePooling3D((1, 1, 5), name='pre_pool')(input_tensor)
x = Convolution3D(4, 2, 2, 3, border_mode='same', name='block1_conv1')(x)
# x = BatchNormalization(axis=1)(x)
x = Activation('relu')(x)
x = MaxPooling3D((2, 2, 3), name='block1_pool')(x)
# Block 2
x = Convolution3D(8, 2, 2, 3, border_mode='same', name='block2_conv1')(x)
# x = BatchNormalization(axis=1)(x)
x = Activation('relu')(x)
x = MaxPooling3D(( 1, 1, 3), name='block2_pool')(x)
# Block 3
x = Convolution3D(16, 2,2, 3, border_mode='same', name='block3_conv1')(x)
# x = BatchNormalization(axis=1)(x)
x = Activation('relu')(x)
x = MaxPooling3D((1, 1, 2), name='block3_pool')(x)
# Block 4
# x = Convolution2D(32, 1, 3, border_mode='same', name='block4_conv1')(x)
# x = BatchNormalization(axis=1)(x)
# x = Activation('relu')(x)
# x = MaxPooling2D((1, 2), name='block4_pool')(x)
x = Flatten(name='flatten')(x)
x = Dropout(0.5)(x)
x = Dense(64, W_regularizer=l2(0.01), name='fc1')(x)
#x = BatchNormalization()(x)
#x = Activation('relu')(x)
#x = Dropout(0.5)(x)
#x = Dense(1, name='predictions')(x)
# x = BatchNormalization()(x)
predictions = Activation('sigmoid')(x)
# for layer in base_model.layers[:10]:
# layer.trainable = False
model = Model(input=input_tensor, output=predictions)
if weights is not None:
model.load_weights(weights)
return model
def pooling(layer, layer_in, layerId):
poolMap = {
('1D', 'MAX'): MaxPooling1D,
('2D', 'MAX'): MaxPooling2D,
('3D', 'MAX'): MaxPooling3D,
('1D', 'AVE'): AveragePooling1D,
('2D', 'AVE'): AveragePooling2D,
('3D', 'AVE'): AveragePooling3D,
}
out = {}
layer_type = layer['params']['layer_type']
pool_type = layer['params']['pool']
padding = get_padding(layer)
if (layer_type == '1D'):
strides = layer['params']['stride_w']
kernel = layer['params']['kernel_w']
if (padding == 'custom'):
p_w = layer['params']['pad_w']
out[layerId + 'Pad'] = ZeroPadding1D(padding=p_w)(*layer_in)
padding = 'valid'
layer_in = [out[layerId + 'Pad']]
elif (layer_type == '2D'):
strides = (layer['params']['stride_h'], layer['params']['stride_w'])
kernel = (layer['params']['kernel_h'], layer['params']['kernel_w'])
if (padding == 'custom'):
p_h, p_w = layer['params']['pad_h'], layer['params']['pad_w']
out[layerId + 'Pad'] = ZeroPadding2D(padding=(p_h, p_w))(*layer_in)
padding = 'valid'
layer_in = [out[layerId + 'Pad']]
else:
strides = (layer['params']['stride_h'], layer['params']['stride_w'],
layer['params']['stride_d'])
kernel = (layer['params']['kernel_h'], layer['params']['kernel_w'],
layer['params']['kernel_d'])
if (padding == 'custom'):
p_h, p_w, p_d = layer['params']['pad_h'], layer['params']['pad_w'],\
layer['params']['pad_d']
out[layerId + 'Pad'] = ZeroPadding3D(padding=(p_h, p_w, p_d))(*layer_in)
padding = 'valid'
layer_in = [out[layerId + 'Pad']]
out[layerId] = poolMap[(layer_type, pool_type)](pool_size=kernel, strides=strides, padding=padding)(
*layer_in)
return out
# ********** Locally-connected Layers **********
