def create_model_2():
inputs = Input((32, 32, 32, 1))
#noise = GaussianNoise(sigma=0.1)(x)
conv1 = Convolution3D(32, 3, 3, 3, activation='relu', border_mode='same')(inputs)
conv1 = SpatialDropout3D(0.1)(conv1)
conv1 = Convolution3D(64, 3, 3, 3, activation='relu', border_mode='same')(conv1)
pool1 = MaxPooling3D(pool_size=(2,2, 2))(conv1)
x = Flatten()(pool1)
x = Dense(64, init='normal')(x)
x = Dropout(0.5)(x)
predictions = Dense(1, init='normal', activation='sigmoid')(x)
model = Model(input=inputs, output=predictions)
model.summary()
optimizer = Adam(lr=1e-5)
model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['binary_accuracy','precision','recall','mean_squared_error','accuracy'])
return model
python类Convolution3D()的实例源码
def create_model_1():
inputs = Input((32, 32, 32, 1))
#noise = GaussianNoise(sigma=0.1)(x)
conv1 = Convolution3D(32, 3, 3, 3, activation='relu', border_mode='same')(inputs)
conv1 = SpatialDropout3D(0.1)(conv1)
conv1 = Convolution3D(64, 3, 3, 3, activation='relu', border_mode='same')(conv1)
pool1 = MaxPooling3D(pool_size=(2,2, 2))(conv1)
x = Flatten()(pool1)
x = Dense(64, init='normal')(x)
predictions = Dense(1, init='normal', activation='sigmoid')(x)
model = Model(input=inputs, output=predictions)
model.summary()
optimizer = Adam(lr=1e-5)
model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['binary_accuracy','precision','recall','mean_squared_error','accuracy'])
return model
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
def preds3d_baseline(width):
learning_rate = 5e-5
#optimizer = SGD(lr=learning_rate, momentum = 0.9, decay = 1e-3, nesterov = True)
optimizer = Adam(lr=learning_rate)
inputs = Input(shape=(1, 136, 168, 168))
conv1 = Convolution3D(width, 3, 3, 3, activation = 'relu', border_mode='same')(inputs)
conv1 = BatchNormalization(axis = 1)(conv1)
conv1 = Convolution3D(width*2, 3, 3, 3, activation = 'relu', border_mode='same')(conv1)
conv1 = BatchNormalization(axis = 1)(conv1)
pool1 = MaxPooling3D(pool_size=(2, 2, 2), border_mode='same')(conv1)
conv2 = Convolution3D(width*2, 3, 3, 3, activation = 'relu', border_mode='same')(pool1)
conv2 = BatchNormalization(axis = 1)(conv2)
conv2 = Convolution3D(width*4, 3, 3, 3, activation = 'relu', border_mode='same')(conv2)
conv2 = BatchNormalization(axis = 1)(conv2)
pool2 = MaxPooling3D(pool_size=(2, 2, 2), border_mode='same')(conv2)
conv3 = Convolution3D(width*4, 3, 3, 3, activation = 'relu', border_mode='same')(pool2)
conv3 = BatchNormalization(axis = 1)(conv3)
conv3 = Convolution3D(width*8, 3, 3, 3, activation = 'relu', border_mode='same')(conv3)
conv3 = BatchNormalization(axis = 1)(conv3)
pool3 = MaxPooling3D(pool_size=(2, 2, 2), border_mode='same')(conv3)
output = GlobalAveragePooling3D()(pool3)
output = Dense(2, activation='softmax', name = 'predictions')(output)
model3d = Model(inputs, output)
model3d.compile(loss='categorical_crossentropy', optimizer = optimizer, metrics = ['accuracy'])
return model3d
def test_convolution_3d():
nb_samples = 2
nb_filter = 2
stack_size = 3
kernel_dim1 = 2
kernel_dim2 = 3
kernel_dim3 = 1
input_len_dim1 = 10
input_len_dim2 = 11
input_len_dim3 = 12
for border_mode in _convolution_border_modes:
for subsample in [(1, 1, 1), (2, 2, 2)]:
if border_mode == 'same' and subsample != (1, 1, 1):
continue
layer_test(convolutional.Convolution3D,
kwargs={'nb_filter': nb_filter,
'kernel_dim1': kernel_dim1,
'kernel_dim2': kernel_dim2,
'kernel_dim3': kernel_dim3,
'border_mode': border_mode,
'subsample': subsample},
input_shape=(nb_samples,
input_len_dim1, input_len_dim2, input_len_dim3,
stack_size))
layer_test(convolutional.Convolution3D,
kwargs={'nb_filter': nb_filter,
'kernel_dim1': kernel_dim1,
'kernel_dim2': kernel_dim2,
'kernel_dim3': kernel_dim3,
'border_mode': border_mode,
'W_regularizer': 'l2',
'b_regularizer': 'l2',
'activity_regularizer': 'activity_l2',
'subsample': subsample},
input_shape=(nb_samples,
input_len_dim1, input_len_dim2, input_len_dim3,
stack_size))
def create_model_3_noise2():
inputs = Input((32, 32, 32, 1))
noise = GaussianNoise(sigma=0.02)(inputs)
conv1 = Convolution3D(32, 3, 3, 3, activation='relu', border_mode='same')(noise)
conv1 = SpatialDropout3D(0.4)(conv1)
conv1 = Convolution3D(64, 3, 3, 3, activation='relu', border_mode='same')(conv1)
pool1 = MaxPooling3D(pool_size=(2,2, 2))(conv1)
conv2 = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same')(pool1)
conv2 = SpatialDropout3D(0.4)(conv2)
conv2 = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same')(conv2)
pool2 = MaxPooling3D(pool_size=(2,2, 2))(conv2)
x = Flatten()(pool2)
x = Dense(128, init='normal')(x)
x = Dropout(0.5)(x)
x = Dense(64, init='normal')(x)
predictions = Dense(1, init='normal', activation='sigmoid')(x)
model = Model(input=inputs, output=predictions)
model.summary()
optimizer = Adam(lr=0.00001)
model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['binary_accuracy','precision','recall','mean_squared_error','accuracy'])
return model
def create_model_3_noise():
inputs = Input((32, 32, 32, 1))
noise = GaussianNoise(sigma=0.05)(inputs)
conv1 = Convolution3D(32, 3, 3, 3, activation='relu', border_mode='same')(noise)
conv1 = SpatialDropout3D(0.1)(conv1)
conv1 = Convolution3D(64, 3, 3, 3, activation='relu', border_mode='same')(conv1)
pool1 = MaxPooling3D(pool_size=(2,2, 2))(conv1)
conv2 = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same')(pool1)
conv2 = SpatialDropout3D(0.1)(conv2)
conv2 = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same')(conv2)
pool2 = MaxPooling3D(pool_size=(2,2, 2))(conv2)
x = Flatten()(pool2)
x = Dense(64, init='normal')(x)
x = Dropout(0.5)(x)
predictions = Dense(1, init='normal', activation='sigmoid')(x)
model = Model(input=inputs, output=predictions)
model.summary()
optimizer = Adam(lr=0.000001)
model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['binary_accuracy','precision','recall','mean_squared_error','accuracy'])
return model
def create_model_8():
inputs = Input((32, 32, 32, 1))
#noise = GaussianNoise(sigma=0.1)(x)
conv1 = Convolution3D(32, 3, 3, 3, activation='relu', border_mode='same')(inputs)
conv1 = SpatialDropout3D(0.2)(conv1)
conv1 = Convolution3D(64, 3, 3, 3, activation='relu', border_mode='same')(conv1)
conv1 = SpatialDropout3D(0.2)(conv1)
conv1 = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same')(conv1)
pool1 = MaxPooling3D(pool_size=(2,2, 2))(conv1)
conv2 = Convolution3D(256, 3, 3, 3, activation='relu', border_mode='same')(pool1)
conv2 = SpatialDropout3D(0.2)(conv2)
conv2 = Convolution3D(512, 3, 3, 3, activation='relu', border_mode='same')(conv2)
pool2 = MaxPooling3D(pool_size=(2,2, 2))(conv2)
x = Flatten()(pool2)
x = Dense(64, init='normal')(x)
x = Dropout(0.5)(x)
predictions = Dense(1, init='normal', activation='sigmoid')(x)
model = Model(input=inputs, output=predictions)
model.summary()
optimizer = Adam(lr=0.00001)
model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['binary_accuracy','precision','recall','mean_squared_error','accuracy'])
return model
def create_model_7():
inputs = Input((32, 32, 32, 1))
#noise = GaussianNoise(sigma=0.1)(x)
conv1 = Convolution3D(32, 5, 5, 5, activation='relu', border_mode='same')(inputs)
conv1 = SpatialDropout3D(0.1)(conv1)
conv1 = Convolution3D(64, 5, 5, 5, activation='relu', border_mode='same')(conv1)
pool1 = MaxPooling3D(pool_size=(2,2, 2))(conv1)
conv2 = Convolution3D(128, 5, 5, 5, activation='relu', border_mode='same')(pool1)
conv2 = SpatialDropout3D(0.1)(conv2)
conv2 = Convolution3D(128, 5, 5, 5, activation='relu', border_mode='same')(conv2)
pool2 = MaxPooling3D(pool_size=(2,2, 2))(conv2)
x = Flatten()(pool2)
x = Dense(64, init='normal')(x)
x = Dropout(0.5)(x)
predictions = Dense(1, init='normal', activation='sigmoid')(x)
model = Model(input=inputs, output=predictions)
model.summary()
optimizer = Adam(lr=0.00001)
model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['binary_accuracy','precision','recall','mean_squared_error','accuracy'])
return model
def create_model_6():
inputs = Input((32, 32, 32, 1))
#noise = GaussianNoise(sigma=0.1)(x)
conv1 = Convolution3D(32, 3, 3, 3, activation='relu', border_mode='same')(inputs)
conv1 = SpatialDropout3D(0.1)(conv1)
conv1 = Convolution3D(64, 3, 3, 3, activation='relu', border_mode='same')(conv1)
conv1 = SpatialDropout3D(0.1)(conv1)
conv1 = Convolution3D(64, 3, 3, 3, activation='relu', border_mode='same')(conv1)
pool1 = MaxPooling3D(pool_size=(2,2, 2))(conv1)
conv2 = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same')(pool1)
conv2 = SpatialDropout3D(0.1)(conv2)
conv2 = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same')(conv2)
conv2 = SpatialDropout3D(0.1)(conv2)
conv2 = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same')(conv2)
pool2 = MaxPooling3D(pool_size=(2,2, 2))(conv2)
x = Flatten()(pool2)
x = Dense(64, init='normal')(x)
x = Dropout(0.5)(x)
predictions = Dense(1, init='normal', activation='sigmoid')(x)
model = Model(input=inputs, output=predictions)
model.summary()
optimizer = Adam(lr=0.00001)
model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['binary_accuracy','precision','recall','mean_squared_error','accuracy'])
return model
def create_model_4():
inputs1 = Input((32, 32, 32, 1))
inputs2 = Input((6,))
#noise = GaussianNoise(sigma=0.1)(x)
conv1 = Convolution3D(32, 3, 3, 3, activation='relu', border_mode='same')(inputs1)
conv1 = SpatialDropout3D(0.1)(conv1)
conv1 = Convolution3D(64, 3, 3, 3, activation='relu', border_mode='same')(conv1)
pool1 = MaxPooling3D(pool_size=(2,2, 2))(conv1)
conv2 = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same')(pool1)
conv2 = SpatialDropout3D(0.1)(conv2)
conv2 = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same')(conv2)
pool2 = MaxPooling3D(pool_size=(2,2, 2))(conv2)
x = Flatten()(pool2)
x = merge([x, inputs2], mode='concat')
x = Dense(64, init='normal')(x)
x = Dropout(0.5)(x)
predictions = Dense(1, init='normal', activation='sigmoid')(x)
model = Model(input=[inputs1,inputs2], output=predictions)
model.summary()
optimizer = Adam()
model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['binary_accuracy','precision','recall','mean_squared_error','accuracy'])
return model
def create_model_3():
inputs = Input((32, 32, 32, 1))
#noise = GaussianNoise(sigma=0.1)(x)
conv1 = Convolution3D(32, 3, 3, 3, activation='relu', border_mode='same')(inputs)
conv1 = SpatialDropout3D(0.1)(conv1)
conv1 = Convolution3D(64, 3, 3, 3, activation='relu', border_mode='same')(conv1)
pool1 = MaxPooling3D(pool_size=(2,2, 2))(conv1)
conv2 = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same')(pool1)
conv2 = SpatialDropout3D(0.1)(conv2)
conv2 = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same')(conv2)
pool2 = MaxPooling3D(pool_size=(2,2, 2))(conv2)
x = Flatten()(pool2)
x = Dense(64, init='normal')(x)
x = Dropout(0.5)(x)
predictions = Dense(1, init='normal', activation='sigmoid')(x)
model = Model(input=inputs, output=predictions)
model.summary()
optimizer = Adam(lr=0.00001)
model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['binary_accuracy','precision','recall','mean_squared_error','accuracy'])
return model
def test_convolution_3d():
nb_samples = 2
nb_filter = 2
stack_size = 3
kernel_dim1 = 2
kernel_dim2 = 3
kernel_dim3 = 1
input_len_dim1 = 10
input_len_dim2 = 11
input_len_dim3 = 12
for border_mode in _convolution_border_modes:
for subsample in [(1, 1, 1), (2, 2, 2)]:
if border_mode == 'same' and subsample != (1, 1, 1):
continue
layer_test(convolutional.Convolution3D,
kwargs={'nb_filter': nb_filter,
'kernel_dim1': kernel_dim1,
'kernel_dim2': kernel_dim2,
'kernel_dim3': kernel_dim3,
'border_mode': border_mode,
'subsample': subsample},
input_shape=(nb_samples,
input_len_dim1, input_len_dim2, input_len_dim3,
stack_size))
layer_test(convolutional.Convolution3D,
kwargs={'nb_filter': nb_filter,
'kernel_dim1': kernel_dim1,
'kernel_dim2': kernel_dim2,
'kernel_dim3': kernel_dim3,
'border_mode': border_mode,
'W_regularizer': 'l2',
'b_regularizer': 'l2',
'activity_regularizer': 'activity_l2',
'subsample': subsample},
input_shape=(nb_samples,
input_len_dim1, input_len_dim2, input_len_dim3,
stack_size))
def test_convolution_3d():
nb_samples = 2
nb_filter = 2
stack_size = 3
kernel_dim1 = 2
kernel_dim2 = 3
kernel_dim3 = 1
input_len_dim1 = 10
input_len_dim2 = 11
input_len_dim3 = 12
for border_mode in _convolution_border_modes:
for subsample in [(1, 1, 1), (2, 2, 2)]:
if border_mode == 'same' and subsample != (1, 1, 1):
continue
layer_test(convolutional.Convolution3D,
kwargs={'nb_filter': nb_filter,
'kernel_dim1': kernel_dim1,
'kernel_dim2': kernel_dim2,
'kernel_dim3': kernel_dim3,
'border_mode': border_mode,
'subsample': subsample},
input_shape=(nb_samples,
input_len_dim1, input_len_dim2, input_len_dim3,
stack_size))
layer_test(convolutional.Convolution3D,
kwargs={'nb_filter': nb_filter,
'kernel_dim1': kernel_dim1,
'kernel_dim2': kernel_dim2,
'kernel_dim3': kernel_dim3,
'border_mode': border_mode,
'W_regularizer': 'l2',
'b_regularizer': 'l2',
'activity_regularizer': 'activity_l2',
'subsample': subsample},
input_shape=(nb_samples,
input_len_dim1, input_len_dim2, input_len_dim3,
stack_size))
def preds3d_baseline(width):
learning_rate = 5e-5
optimizer = SGD(lr=learning_rate, momentum = 0.9, decay = 1e-3, nesterov = True)
#optimizer = Adam(lr=learning_rate)
inputs = Input(shape=(1, 136, 168, 168))
conv1 = Convolution3D(width, 3, 3, 3, activation = 'relu', border_mode='same')(inputs)
conv1 = BatchNormalization(axis = 1)(conv1)
conv1 = Convolution3D(width*2, 3, 3, 3, activation = 'relu', border_mode='same')(conv1)
conv1 = BatchNormalization(axis = 1)(conv1)
pool1 = MaxPooling3D(pool_size=(2, 2, 2), border_mode='same')(conv1)
conv2 = Convolution3D(width*2, 3, 3, 3, activation = 'relu', border_mode='same')(pool1)
conv2 = BatchNormalization(axis = 1)(conv2)
conv2 = Convolution3D(width*4, 3, 3, 3, activation = 'relu', border_mode='same')(conv2)
conv2 = BatchNormalization(axis = 1)(conv2)
pool2 = MaxPooling3D(pool_size=(2, 2, 2), border_mode='same')(conv2)
conv3 = Convolution3D(width*4, 3, 3, 3, activation = 'relu', border_mode='same')(pool2)
conv3 = BatchNormalization(axis = 1)(conv3)
conv3 = Convolution3D(width*8, 3, 3, 3, activation = 'relu', border_mode='same')(conv3)
conv3 = BatchNormalization(axis = 1)(conv3)
pool3 = MaxPooling3D(pool_size=(2, 2, 2), border_mode='same')(conv3)
output = GlobalAveragePooling3D()(pool3)
output = Dense(2, activation='softmax', name = 'predictions')(output)
model3d = Model(inputs, output)
model3d.compile(loss='categorical_crossentropy', optimizer = optimizer, metrics = ['accuracy'])
return model3d
# 1398 stage1 original examples
def preds3d_globalavg(width):
learning_rate = 5e-5
#optimizer = SGD(lr=learning_rate, momentum = 0.9, decay = 1e-3, nesterov = True)
optimizer = Adam(lr=learning_rate)
inputs = Input(shape=(1, 136, 168, 168))
conv1 = Convolution3D(width, 3, 3, 3, activation = 'relu', border_mode='same')(inputs)
conv1 = BatchNormalization(axis = 1)(conv1)
conv1 = Convolution3D(width*2, 3, 3, 3, activation = 'relu', border_mode='same')(conv1)
conv1 = BatchNormalization(axis = 1)(conv1)
pool1 = MaxPooling3D(pool_size=(2, 2, 2), border_mode='same')(conv1)
conv2 = Convolution3D(width*2, 3, 3, 3, activation = 'relu', border_mode='same')(pool1)
conv2 = BatchNormalization(axis = 1)(conv2)
conv2 = Convolution3D(width*4, 3, 3, 3, activation = 'relu', border_mode='same')(conv2)
conv2 = BatchNormalization(axis = 1)(conv2)
pool2 = MaxPooling3D(pool_size=(2, 2, 2), border_mode='same')(conv2)
conv3 = Convolution3D(width*4, 3, 3, 3, activation = 'relu', border_mode='same')(pool2)
conv3 = BatchNormalization(axis = 1)(conv3)
conv3 = Convolution3D(width*8, 3, 3, 3, activation = 'relu', border_mode='same')(conv3)
conv3 = BatchNormalization(axis = 1)(conv3)
pool3 = MaxPooling3D(pool_size=(2, 2, 2), border_mode='same')(conv3)
conv4 = Convolution3D(width*8, 3, 3, 3, activation = 'relu', border_mode='same')(pool3)
conv4 = BatchNormalization(axis = 1)(conv4)
conv4 = Convolution3D(width*16, 3, 3, 3, activation = 'relu', border_mode='same')(conv4)
conv4 = BatchNormalization(axis = 1)(conv4)
pool4 = MaxPooling3D(pool_size=(8, 8, 8), border_mode='same')(conv4)
output = GlobalAveragePooling3D()(conv4)
output = Dense(2, activation='softmax', name = 'predictions')(output)
model3d = Model(inputs, output)
model3d.compile(loss='categorical_crossentropy', optimizer = optimizer, metrics = ['accuracy'])
return model3d
def unet_model():
inputs = Input(shape=(1, max_slices, img_size, img_size))
conv1 = Convolution3D(width, 3, 3, 3, activation = 'relu', border_mode='same')(inputs)
conv1 = BatchNormalization(axis = 1)(conv1)
conv1 = Convolution3D(width*2, 3, 3, 3, activation = 'relu', border_mode='same')(conv1)
conv1 = BatchNormalization(axis = 1)(conv1)
pool1 = MaxPooling3D(pool_size=(2, 2, 2), strides = (2, 2, 2), border_mode='same')(conv1)
conv2 = Convolution3D(width*2, 3, 3, 3, activation = 'relu', border_mode='same')(pool1)
conv2 = BatchNormalization(axis = 1)(conv2)
conv2 = Convolution3D(width*4, 3, 3, 3, activation = 'relu', border_mode='same')(conv2)
conv2 = BatchNormalization(axis = 1)(conv2)
pool2 = MaxPooling3D(pool_size=(2, 2, 2), strides = (2, 2, 2), border_mode='same')(conv2)
conv3 = Convolution3D(width*4, 3, 3, 3, activation = 'relu', border_mode='same')(pool2)
conv3 = BatchNormalization(axis = 1)(conv3)
conv3 = Convolution3D(width*8, 3, 3, 3, activation = 'relu', border_mode='same')(conv3)
conv3 = BatchNormalization(axis = 1)(conv3)
pool3 = MaxPooling3D(pool_size=(2, 2, 2), strides = (2, 2, 2), border_mode='same')(conv3)
conv4 = Convolution3D(width*8, 3, 3, 3, activation = 'relu', border_mode='same')(pool3)
conv4 = BatchNormalization(axis = 1)(conv4)
conv4 = Convolution3D(width*8, 3, 3, 3, activation = 'relu', border_mode='same')(conv4)
conv4 = BatchNormalization(axis = 1)(conv4)
conv4 = Convolution3D(width*16, 3, 3, 3, activation = 'relu', border_mode='same')(conv4)
conv4 = BatchNormalization(axis = 1)(conv4)
up5 = merge([UpSampling3D(size=(2, 2, 2))(conv4), conv3], mode='concat', concat_axis=1)
conv5 = SpatialDropout3D(dropout_rate)(up5)
conv5 = Convolution3D(width*8, 3, 3, 3, activation = 'relu', border_mode='same')(conv5)
conv5 = Convolution3D(width*8, 3, 3, 3, activation = 'relu', border_mode='same')(conv5)
up6 = merge([UpSampling3D(size=(2, 2, 2))(conv5), conv2], mode='concat', concat_axis=1)
conv6 = SpatialDropout3D(dropout_rate)(up6)
conv6 = Convolution3D(width*4, 3, 3, 3, activation = 'relu', border_mode='same')(conv6)
conv6 = Convolution3D(width*4, 3, 3, 3, activation = 'relu', border_mode='same')(conv6)
up7 = merge([UpSampling3D(size=(2, 2, 2))(conv6), conv1], mode='concat', concat_axis=1)
conv7 = SpatialDropout3D(dropout_rate)(up7)
conv7 = Convolution3D(width*2, 3, 3, 3, activation = 'relu', border_mode='same')(conv7)
conv7 = Convolution3D(width*2, 3, 3, 3, activation = 'relu', border_mode='same')(conv7)
conv8 = Convolution3D(1, 1, 1, 1, activation='sigmoid')(conv7)
model = Model(input=inputs, output=conv8)
model.compile(optimizer=Adam(lr=1e-5),
loss=dice_coef_loss, metrics=[dice_coef])
return model
def preds3d_dense(width):
learning_rate = 5e-5
#optimizer = SGD(lr=learning_rate, momentum = 0.9, decay = 1e-3, nesterov = True)
optimizer = Adam(lr=learning_rate)
inputs = Input(shape=(1, 136, 168, 168))
conv1 = Convolution3D(width, 3, 3, 3, activation = 'relu', border_mode='same')(inputs)
conv1 = BatchNormalization(axis = 1)(conv1)
conv1 = Convolution3D(width*2, 3, 3, 3, activation = 'relu', border_mode='same')(conv1)
conv1 = BatchNormalization(axis = 1)(conv1)
pool1 = MaxPooling3D(pool_size=(2, 2, 2), border_mode='same')(conv1)
conv2 = Convolution3D(width*2, 3, 3, 3, activation = 'relu', border_mode='same')(pool1)
conv2 = BatchNormalization(axis = 1)(conv2)
conv2 = Convolution3D(width*4, 3, 3, 3, activation = 'relu', border_mode='same')(conv2)
conv2 = BatchNormalization(axis = 1)(conv2)
pool2 = MaxPooling3D(pool_size=(2, 2, 2), border_mode='same')(conv2)
conv3 = Convolution3D(width*4, 3, 3, 3, activation = 'relu', border_mode='same')(pool2)
conv3 = BatchNormalization(axis = 1)(conv3)
conv3 = Convolution3D(width*8, 3, 3, 3, activation = 'relu', border_mode='same')(conv3)
conv3 = BatchNormalization(axis = 1)(conv3)
pool3 = MaxPooling3D(pool_size=(2, 2, 2), border_mode='same')(conv3)
conv4 = Convolution3D(width*8, 3, 3, 3, activation = 'relu', border_mode='same')(pool3)
conv4 = BatchNormalization(axis = 1)(conv4)
conv4 = Convolution3D(width*16, 3, 3, 3, activation = 'relu', border_mode='same')(conv4)
conv4 = BatchNormalization(axis = 1)(conv4)
pool4 = MaxPooling3D(pool_size=(8, 8, 8), border_mode='same')(conv4)
output = Flatten(name='flatten')(pool4)
output = Dropout(0.2)(output)
output = Dense(128)(output)
output = PReLU()(output)
output = BatchNormalization()(output)
output = Dropout(0.2)(output)
output = Dense(128)(output)
output = PReLU()(output)
output = BatchNormalization()(output)
output = Dropout(0.3)(output)
output = Dense(2, activation='softmax', name = 'predictions')(output)
model3d = Model(inputs, output)
model3d.compile(loss='categorical_crossentropy', optimizer = optimizer, metrics = ['accuracy'])
return model3d
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
