Keras: Error when checking input - keras

Question

I am using Keras autoencodes with Theano backend. And want to make autoencode for 720x1080 RGB images.
This is my code
from keras.datasets import mnist
import numpy as np
from keras.layers import Input, LSTM, RepeatVector, Conv2D, MaxPooling2D, UpSampling2D
from keras.models import Model
from PIL import Image
x_train = []
x_train_noisy = []
for i in range(5,1000):
image = Image.open('data/trailerframes/frame' + str(i) + '.jpg', 'r')
x_train.append(np.array(image))
image = Image.open('data/trailerframes_avg/frame' + str(i) + '.jpg', 'r')
x_train_noisy.append(np.array(image))
x_train = np.array(x_train)
x_train = x_train.astype('float32') / 255.
x_train_noisy = np.array(x_train_noisy)
x_train_noisy = x_train_noisy.astype('float32') / 255.
input_img = Input(shape=(720, 1080, 3))
x = Conv2D(32, (3, 3), activation='relu', padding='same')(input_img)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)
encoded = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(32, (3, 3), data_format="channels_last", activation='relu', padding='same')(encoded)
x = UpSampling2D((2, 2))(x)
x = Conv2D(32, (3, 3), data_format="channels_last", activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
decoded = Conv2D(1, (3, 3), data_format="channels_last", activation='sigmoid', padding='same')(x)
autoencoder = Model(input_img, decoded)
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')
autoencoder.fit(x_train_noisy, x_train,
epochs=10,
batch_size=128,
shuffle=True,
validation_data=(x_train_noisy, x_train))
But it gives me an error
ValueError: Error when checking input: expected input_7 to have shape (None, 720, 1080, 3) but got array with shape (995, 720, 1280, 3)

Answer 1

Input error:
As simple as:
You defined your input as (720,1080,3)
You're trying to trian your model with data in the form (720,1280,3)
One of them is wrong, and I think it's a typo in the input:
#change 1080 for 1280
input_img = Input(shape=(720, 1280, 3))
Output error (target):
Now, your target data is shaped like (720,1280,3), and your last layer outputs (720,1280,1)
A simple fix is:
decoded = Conv2D(3, (3, 3), data_format="channels_last", activation='sigmoid', padding='same')(x)
Using the encoder:
After training that model, you can create submodels for using only the encoder or the decoder:
encoderModel = Model(input_img, decoded)
decoderInput = Input((shape of the encoder output))
decoderModel = Model(decoderInput,decoded))
These two models will share the exact same weights of the entire model, training one model will affect all three models.
For using them without training, you can use model.predict(data), which will give you the results without training.