def get_deconv1d(conv1d_layer: Conv1D):
W = conv1d_layer.get_weights()[0]
# W: kernel_width, kernel_depth, n_filters
# Reverse the conv operation
W = np.transpose(W, (0, 2, 1))
# Transpose the columns and rows
W = W[::-1, :, :]
n_filters = W.shape[2]
kernel_size = W.shape[0]
strides = conv1d_layer.strides
padding = conv1d_layer.padding
b = np.zeros(n_filters)
return DeConv1D(n_filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
kernel_initializer=tf.constant_initializer(W),
bias_initializer=tf.constant_initializer(b),
trainable=False)
def get_deconv1d_reverse_bias(conv1d_layer: Conv1D):
return ReverseBiasLayer(conv1d_layer.get_weights()[1])
