1D CNN input shape and the training data shape - keras

Question

I am getting error trying to feed in the following data into my network. I have issue with reshaping the training data and the input to the network. The error I am getting is:
Error when checking target: expected conv1d_92 to have shape (4, 1) but got array with shape (1, 784)
The code is as follows:
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 17 20:57:51 2021
#author: morte
"""
import keras
from keras import layers
from keras.datasets import mnist
import numpy as np
#(x_train, _), (x_test, _) = mnist.load_data()
#x_train = x_train.astype('float32') / 255.
#x_test = x_test.astype('float32') / 255.
#x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))
#x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))
(x_train, _), (x_test, _) = mnist.load_data()
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = np.reshape(x_train, (len(x_train),1,28*28)) #
x_test = np.reshape(x_test, (len(x_test),1,28*28)) #
input_img = keras.Input(shape=(x_train.shape[1:]))
x = layers.Conv1D(16,(3), activation='relu', padding='same')(input_img)
x = layers.MaxPooling1D(2, padding='same')(x)
x = layers.Conv1D(8,(3), activation='relu', padding='same')(x)
x = layers.MaxPooling1D(2, padding='same')(x)
x = layers.Conv1D(8,(3), activation='relu', padding='same')(x)
encoded = layers.MaxPooling1D(2, padding='same')(x)
# at this point the representation is (4, 4, 8) i.e. 128-dimensional
x = layers.Conv1D(8,(3), activation='relu', padding='same')(encoded)
x = layers.UpSampling1D(2)(x)
x = layers.Conv1D(8,(3), activation='relu', padding='same')(x)
x = layers.UpSampling1D(2)(x)
x = layers.Conv1D(16,(3), activation='relu')(x)
x = layers.UpSampling1D(2)(x)
decoded = layers.Conv1D(1, (3), activation='sigmoid', padding='same')(x)
autoencoder = keras.Model(input_img, decoded)
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')
from keras.datasets import mnist
import numpy as np
from keras.callbacks import TensorBoard
autoencoder.fit(x_train, x_train,
epochs=2,
batch_size=128,
shuffle=True,
validation_data=(x_test, x_test),
)
decoded_imgs = autoencoder.predict(x_test)
import matplotlib.pyplot as plt
n = 10
plt.figure(figsize=(20, 4))
for i in range(1, n + 1):
# Display original
ax = plt.subplot(2, n, i)
plt.imshow(x_test[i].reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
# Display reconstruction
ax = plt.subplot(2, n, i + n)
plt.imshow(decoded_imgs[i].reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()

Answer 1

Since it is an Autoencoder Network, Encoder-Input has to match Decoder-Output.
You are giving a (1,785) shaped Input-array and outputting an (4,1) array.
For more details you can add the line: autoencoder.summary() (for example after the line autoencoder = keras.Model(input_img, decoded))
This will give you information about the shapes of every layer.
An approach would be for example add a Dense Layer at the end of your Decoder.