def keras_dropout(layer, rate):
input_dim = len(layer.input.shape)
if input_dim == 2:
return layers.SpatialDropout1D(rate)
elif input_dim == 3:
return layers.SpatialDropout2D(rate)
elif input_dim == 4:
return layers.SpatialDropout3D(rate)
else:
return layers.Dropout(rate)
def keras_dropout(layer, rate):
'''keras dropout layer.
'''
from keras import layers
input_dim = len(layer.input.shape)
if input_dim == 2:
return layers.SpatialDropout1D(rate)
elif input_dim == 3:
return layers.SpatialDropout2D(rate)
elif input_dim == 4:
return layers.SpatialDropout3D(rate)
else:
return layers.Dropout(rate)
activation='relu',
name='conv1t_1')(x1)
conv1tMP_1 = layers.MaxPooling3D(pool_size=(1, 4, 4),
strides=None,
padding='valid',
data_format=None)(conv1t_1)
conv1tNorm_1 = layers.BatchNormalization(
epsilon=0.001,
axis=-1,
momentum=0.99,
weights=None,
beta_init='zero',
gamma_init='one',
gamma_regularizer=None,
beta_regularizer=None)(conv1tMP_1)
conv1tDO_1 = layers.SpatialDropout3D(0.3)(conv1tNorm_1)
conv1_1 = layers.Conv3D(filters=32,
kernel_size=(3, 5, 5),
strides=1,
padding='valid',
activation='relu',
name='conv1_1')(conv1tDO_1)
conv1MP_1 = layers.MaxPooling3D(pool_size=(1, 4, 2),
strides=None,
padding='valid',
data_format=None)(conv1_1)
conv1Norm_1 = layers.BatchNormalization(
epsilon=0.001,
axis=-1,
momentum=0.99,
weights=None,
padding='valid',
activation='relu',
name='conv1t')(x)
conv1tMP = layers.MaxPooling3D(pool_size=(1, 3, 4),
strides=None,
padding='valid',
data_format=None)(conv1t)
conv1tNorm = layers.BatchNormalization(epsilon=0.001,
axis=-1,
momentum=0.99,
weights=None,
beta_init='zero',
gamma_init='one',
gamma_regularizer=None,
beta_regularizer=None)(conv1tMP)
conv1tDO = layers.SpatialDropout3D(0.3)(conv1tNorm)
conv1 = layers.Conv3D(filters=32,
kernel_size=(3, 5, 5),
strides=1,
padding='valid',
activation='relu',
name='conv1')(conv1tDO)
conv1MP = layers.MaxPooling3D(pool_size=(1, 4, 3),
strides=None,
padding='valid',
data_format=None)(conv1)
conv1Norm = layers.BatchNormalization(epsilon=0.001,
axis=-1,
momentum=0.99,
weights=None,
beta_init='zero',
def D3GenerateModel(n_filter=16,
number_of_class=1,
input_shape=(16, 144, 144, 1),
activation_last='softmax',
metrics=[
'mse', 'acc', dice_coef, recall_at_thresholds,
precision_at_thresholds, auc_roc
],
loss='binary_crossentropy',
dropout=0.05,
init='glorot_uniform',
two_output=False):
filter_size = n_filter
input_x = layers.Input(shape=input_shape,
name='Input_layer',
dtype='float32')
#1 level
x = layers.Conv3D(filters=filter_size,
kernel_size=(3, 3, 3),
strides=(1, 1, 1),
padding='same')(input_x)
x = layers.LeakyReLU()(x)
x = layers.Conv3D(filters=filter_size * 2,
kernel_size=(3, 3, 3),
strides=(1, 1, 1),
padding='same')(x)
x = layers.LeakyReLU()(x)
x = layers.MaxPooling3D(pool_size=(2, 2, 2), padding='same')(x)
#2 level
conv_list = []
counter = 0
for index, kernel_sizes in enumerate([[(1, 3, 3), (1, 1, 3)],
[(3, 3, 3), (3, 1, 3)],
[(3, 3, 1), (1, 3, 1)]]):
for kernel_size in (kernel_sizes):
x = layers.Conv3D(filters=(filter_size * 4),
kernel_size=kernel_size,
strides=(1, 1, 1),
padding='same',
name='Conv3D_%s' % (counter))(x)
x = layers.BatchNormalization()(x)
x = layers.LeakyReLU()(x)
x = layers.SpatialDropout3D(dropout)(x)
counter = counter + 1
conv_list.append(x)
x = layers.add(conv_list)
x = layers.Conv3D(filters=filter_size * 8,
kernel_size=(3, 3, 3),
strides=(2, 2, 2),
padding='same')(x)
x = layers.Reshape(target_shape=[4, -1, filter_size * 8])(x)
x = layers.Conv2D(filters=filter_size * 8,
kernel_size=(1, 1296),
strides=(1, 1296))(x)
x = layers.BatchNormalization()(x)
x = layers.ReLU()(x)
#x = layers.SpatialDropout2D(dropout)(x)
#x = layers.Lambda(squash)(x)
#x = layers.Softmax()(x)
x = layers.Reshape(target_shape=[filter_size * 8, -1])(x)
x = layers.Conv1D(filters=2,
kernel_size=filter_size * 8,
strides=filter_size * 8,
activation='softmax')(
x) #, kernel_regularizer=l2(0.001))(x)
y = layers.Flatten()(x)
#Classification
model = Model(inputs=input_x, outputs=y)
#keras.optimizers.SGD(lr=lr, momentum=0.90, decay=decay, nesterov=False)
opt_noise = add_gradient_noise(optimizers.Adam)
optimizer = opt_noise(
lr, amsgrad=True) #, nesterov=True)#opt_noise(lr, amsgrad=True)
model.compile(optimizer=optimizer, loss=loss,
metrics=metrics) #categorical_crossentropy
return model
