I am building an autoencoder for learning 28 ultrasound time signals of shape [262144,2] i.e. 262144 pairs of time and voltage points concatenated to form a [262144x2] tensor as input data to a stacked convolutional encoder. The latent space is set to produce a vector of length 16. The problem arise from the decoder, where a 'for loop' is used to stack two Conv2DTranspose layers each with a filter sizes of 64 and 32 and a kernel of 3 to the latent space output in order to reproduce the original input shape of [262144x2]. Instead, the decoder network gives a [262144x4] output tensor which does not match the validation and input data shapes of [262144x2]. What model parameters (filter, kernel, strides and padding) should I use to get appropriate tensor dimensions? the code and output are shown below. Your assistance is greatly appreciated!
from keras.layers import Dense, Input
from keras.layers import Conv2D, Flatten
from keras.layers import Reshape, Conv2DTranspose
from keras.models import Model
from keras.datasets import mnist
from keras.utils import plot_model
from keras import backend as K
import numpy as np
import matplotlib.pyplot as plt
x_Train = Signals
x_Test = Signals1
Sig_size1 = x_Train.shape[1]
Sig_size2 = x_Train.shape[2]
Sig_size11 = x_Test.shape[1]
Sig_size22 = x_Test.shape[2]
x_Train = np.reshape(x_Train,[-1, Sig_size1, Sig_size2, 1])
x_Train = x_Train.astype('float32') / np.max(x_Train)
x_Test = np.reshape(x_Test,[-1, Sig_size11, Sig_size22, 1])
x_Test = x_Test.astype('float32') / np.max(x_Test)
# network parameters# encoder/decoder number of filters per CNN layer
input_shape = (Sig_size1, Sig_size2, 1)
batch_size = 32 # Was 32
kernel_size = 1 # Was 3
latent_dim = 16 # Was 16
# encoder/decoder number of filters per CNN layer
layer_filters = [2, 6]
# build the autoencoder model
# first build the encoder model
inputs = Input(shape=input_shape, name='encoder_input')
x = inputs
# stack of Conv2D(32)-Conv2D(64)
for filters in layer_filters:
x = Conv2D(filters=filters,
kernel_size=kernel_size,
strides=2,
activation='relu',
padding='same')(x)
shape = K.int_shape(x)
# generate latent vector
x = Flatten()(x)
latent = Dense(latent_dim, name='latent_vector')(x)
# instantiate encoder model
encoder = Model(inputs, latent, name='encoder')
encoder.summary()
plot_model(encoder, to_file='encoder.png', show_shapes=True)
# build the decoder model
latent_inputs = Input(shape=(latent_dim,), name='decoder_input')
# use the shape (7, 7, 64) that was earlier saved
x = Dense(shape[1] * shape[2] * shape[3])(latent_inputs)
# from vector to suitable shape for transposed conv
x = Reshape((shape[1], shape[2], shape[3]))(x)
# stack of Conv2DTranspose(64)-Conv2DTranspose(32)
layer_filters = [1,8] ########change
kernel_size = 3 # Was 3
# for filters in layer_filters[::-1]:
for filters in layer_filters[::-1]:
x = Conv2DTranspose(filters=filters,
kernel_size=kernel_size,
strides=2,
activation='relu',
padding='same')(x)
# layer_filters = [64, 32]
kernel_size = 3 # Was 3
# reconstruct the input
outputs = Conv2DTranspose(filters=1, #Was 1
kernel_size=kernel_size,
activation='sigmoid',
padding='same',
name='decoder_output')(x)
# instantiate decoder model
decoder = Model(latent_inputs, outputs, name='decoder')
decoder.summary()
plot_model(decoder, to_file='decoder.png', show_shapes=True)
# autoencoder = encoder + decoder
# instantiate autoencoder model
autoencoder = Model(inputs,
decoder(encoder(inputs)),
name='autoencoder')
autoencoder.summary()
plot_model(autoencoder,
to_file='autoencoder.png',
show_shapes=True)
# Mean Square Error (MSE) loss funtion, Adam optimizer
autoencoder.compile(loss='mse', optimizer='adam')
# train the autoencoder
autoencoder.fit(x_Train,
x_Train,
validation_data=(x_Test, x_Test),
epochs=1,
batch_size=batch_size)
# predict the autoencoder output from test data
x_decoded = autoencoder.predict(x_Test)
This code was adapted from Advanced Deep Learning with Keras by Rowel Atienza (Chapter 3 for a denoising decoder for MNIST data)