def __init__(self):
super(DCSNet, self).__init__()
self.main = nn.Sequential(
# state size. 2 x 1025
ComplexConv1D(1, 16, 7, 2, 3, bias=False),
#nn.BatchNorm2d(ngf * 4),
nn.LeakyReLU(0.2, inplace=True),
# state size. 32 x 513
ComplexConv1D(16, 32, 5, 2, 2, bias=False),
#nn.BatchNorm2d(ngf * 2),
nn.LeakyReLU(0.2, inplace=True),
# state size. 64 x 257
ComplexConv1D(32, 32, 3, 2, 1, bias=False),
#nn.BatchNorm2d(ngf),
nn.LeakyReLU(0.2, inplace=True),
# state size. 64 x 129
ComplexConv1D(32, 64, 3, 2, 1, bias=False),
#nn.BatchNorm2d(ngf),
nn.LeakyReLU(0.2, inplace=True),
# state size. 128 x 65
ComplexConv1D(64, 64, 3, 2, 1, bias=False),
#nn.BatchNorm2d(ngf),
nn.LeakyReLU(0.2, inplace=True)
# state size. 128 x 33
)
self.dense1 = ComplexDense(2112,1025, init_criterion='he')
self.dense2 = ComplexDense(2112,1025, init_criterion='he')
def get_shallow_convnet(window_size=4096, channels=2, output_size=84):
inputs = Input(shape=(window_size, channels))
conv = ComplexConv1D(32, 512, strides=16, activation='relu')(inputs)
pool = AveragePooling1D(pool_size=4, strides=2)(conv)
pool = Permute([2, 1])(pool)
flattened = Flatten()(pool)
dense = ComplexDense(2048, activation='relu')(flattened)
predictions = ComplexDense(output_size,
activation='sigmoid',
bias_initializer=Constant(value=-5))(dense)
predictions = GetReal(predictions)
model = Model(inputs=inputs, outputs=predictions)
model.compile(optimizer=Adam(lr=1e-4),
loss='binary_crossentropy',
metrics=['accuracy'])
return model
def get_deep_convnet(window_size=4096, channels=2, output_size=84):
inputs = Input(shape=(window_size, channels))
outs = inputs
outs = (ComplexConv1D(
16, 6, strides=2, padding='same',
activation='linear',
kernel_initializer='complex_independent'))(outs)
outs = (ComplexBN(axis=-1))(outs)
outs = (keras.layers.Activation('relu'))(outs)
outs = (keras.layers.AveragePooling1D(pool_size=2, strides=2))(outs)
outs = (ComplexConv1D(
32, 3, strides=2, padding='same',
activation='linear',
kernel_initializer='complex_independent'))(outs)
outs = (ComplexBN(axis=-1))(outs)
outs = (keras.layers.Activation('relu'))(outs)
outs = (keras.layers.AveragePooling1D(pool_size=2, strides=2))(outs)
outs = (ComplexConv1D(
64, 3, strides=1, padding='same',
activation='linear',
kernel_initializer='complex_independent'))(outs)
outs = (ComplexBN(axis=-1))(outs)
outs = (keras.layers.Activation('relu'))(outs)
outs = (keras.layers.AveragePooling1D(pool_size=2, strides=2))(outs)
outs = (ComplexConv1D(
64, 3, strides=1, padding='same',
activation='linear',
kernel_initializer='complex_independent'))(outs)
outs = (ComplexBN(axis=-1))(outs)
outs = (keras.layers.Activation('relu'))(outs)
outs = (keras.layers.AveragePooling1D(pool_size=2, strides=2))(outs)
outs = (ComplexConv1D(
128, 3, strides=1, padding='same',
activation='relu',
kernel_initializer='complex_independent'))(outs)
outs = (ComplexConv1D(
128, 3, strides=1, padding='same',
activation='linear',
kernel_initializer='complex_independent'))(outs)
outs = (ComplexBN(axis=-1))(outs)
outs = (keras.layers.Activation('relu'))(outs)
outs = (keras.layers.AveragePooling1D(pool_size=2, strides=2))(outs)
#outs = (keras.layers.MaxPooling1D(pool_size=2))
#outs = (Permute([2, 1]))
outs = (keras.layers.Flatten())(outs)
outs = (keras.layers.Dense(2048, activation='relu',
kernel_initializer='glorot_normal'))(outs)
predictions = (keras.layers.Dense(output_size, activation='sigmoid',
bias_initializer=keras.initializers.Constant(value=-5)))(outs)
model = Model(inputs=inputs, outputs=predictions)
model.compile(optimizer=keras.optimizers.Adam(lr=1e-4),
loss='binary_crossentropy',
metrics=['accuracy'])
return model