def create_sequence(lay_shapes, isConv=True, kernel_shape=3, sampling_rate=2, addBatchNorm=True, addDropout=True,
activate=Activate_CD.relu, last_lay=Sampling_CD.DownSampling):
assert activate in properties(Activate_CD), Messages_CD.USV.format(activate, 'Activations', properties(Activate_CD))
assert activate in properties(Activate_CD), Messages_CD.USV.format(activate, 'Activations', properties(Activate_CD))
x = []
if isConv:
lay_cd = Layer_CD.Conv
else:
lay_cd = Layer_CD.Dense
if len(lay_shapes) %2 != 0:
lay_shapes = lay_shapes[0] + lay_shapes
for i, lay in enumerate(lay_shapes):
x = create_layer(lay_cd, lay, kernel_shape=kernel_shape, addBatchNorm=addBatchNorm, addDropout=addDropout, activate=activate) + x
if isConv:
if i%2 == 0:
x = [Sampling_CD.DownSampling((sampling_rate, sampling_rate), padding='same')] + x
else:
x = [Sampling_CD.UpSampling((sampling_rate, sampling_rate))] + x
x = create_layer(lay_cd, lay, kernel_shape=kernel_shape, addBatchNorm=addBatchNorm, addDropout=addDropout, activate=activate) + x
if last_lay == Sampling_CD.DownSampling:
x = [Sampling_CD.DownSampling((sampling_rate, sampling_rate), padding='same')] + x
else:
x = [Sampling_CD.UpSampling((1, 1), padding='same')] + x
return x
def create_layer(layer_cd,
lay_dim,
kernel_shape=None,
addBatchNorm=True,
addDropout=True,
activate=None):
assert layer_cd in properties(Layer_CD), Messages_CD.USV.format(
layer_cd, 'Layers', properties(Layer_CD))
assert activate in properties(Activate_CD), Messages_CD.USV.format(
activate, 'Activatation', properties(Activate_CD))
x = []
if layer_cd in [Layer_CD.Conv, Layer_CD.Deconv]:
x = [layer_cd(lay_dim, kernel_shape, padding='same')] + x
else:
x = [layer_cd(lay_dim)] + x
if addBatchNorm:
x = [tf.keras.layers.BatchNormalization()] + x
if addDropout:
x = [tf.keras.layers.Dropout(0.2)] + x
if activate:
x = [tf.keras.layers.Activation(activate)] + x
return x
def __init__(self):
keys = list()
items = list()
ddict = dict(default_config.__dict__)
for key, item in ddict.items():
if key in properties(default_config):
keys.append(key)
items.append(item)
ddict = dict(zip(keys, items))
for k, v in ddict.items():
setattr(self, k, v)