Dimension mismatch in Keras during model.fit - keras

Question

I put together a VAE using Dense Neural Networks in Keras. During model.fit I get a dimension mismatch, but not sure what is throwing the code off. Below is what my code looks like
from keras.layers import Lambda, Input, Dense
from keras.models import Model
from keras.datasets import mnist
from keras.losses import mse, binary_crossentropy
from keras.utils import plot_model
from keras import backend as K
import keras
import numpy as np
import matplotlib.pyplot as plt
import argparse
import os
(x_train, y_train), (x_test, y_test) = mnist.load_data()
image_size = x_train.shape[1]
original_dim = image_size * image_size
x_train = np.reshape(x_train, [-1, original_dim])
x_test = np.reshape(x_test, [-1, original_dim])
x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255
# network parameters
input_shape = (original_dim, )
intermediate_dim = 512
batch_size = 128
latent_dim = 2
epochs = 50
x = Input(batch_shape=(batch_size, original_dim))
h = Dense(intermediate_dim, activation='relu')(x)
z_mean = Dense(latent_dim)(h)
z_log_sigma = Dense(latent_dim)(h)
def sampling(args):
z_mean, z_log_sigma = args
#epsilon = K.random_normal(shape=(batch, dim))
epsilon = K.random_normal(shape=(batch_size, latent_dim))
return z_mean + K.exp(z_log_sigma) * epsilon
# note that "output_shape" isn't necessary with the TensorFlow backend
# so you could write `Lambda(sampling)([z_mean, z_log_sigma])`
z = Lambda(sampling, output_shape=(latent_dim,))([z_mean, z_log_sigma])
decoder_h = Dense(intermediate_dim, activation='relu')
decoder_mean = Dense(original_dim, activation='sigmoid')
h_decoded = decoder_h(z)
x_decoded_mean = decoder_mean(h_decoded)
print('X Decoded Mean shape: ', x_decoded_mean.shape)
# end-to-end autoencoder
vae = Model(x, x_decoded_mean)
# encoder, from inputs to latent space
encoder = Model(x, z_mean)
# generator, from latent space to reconstructed inputs
decoder_input = Input(shape=(latent_dim,))
_h_decoded = decoder_h(decoder_input)
_x_decoded_mean = decoder_mean(_h_decoded)
generator = Model(decoder_input, _x_decoded_mean)
def vae_loss(x, x_decoded_mean):
xent_loss = keras.metrics.binary_crossentropy(x, x_decoded_mean)
kl_loss = - 0.5 * K.mean(1 + z_log_sigma - K.square(z_mean) - K.exp(z_log_sigma), axis=-1)
return xent_loss + kl_loss
vae.compile(optimizer='rmsprop', loss=vae_loss)
print('X train shape: ', x_train.shape)
print('X test shape: ', x_test.shape)
vae.fit(x_train, x_train,
shuffle=True,
epochs=epochs,
batch_size=batch_size,
validation_data=(x_test, x_test))
Here is the stack trace that I see when model.fit is called.
File "/home/asattar/workspace/projects/keras-examples/blogautoencoder/VariationalAutoEncoder.py", line 81, in <module>
validation_data=(x_test, x_test))
File "/usr/local/lib/python2.7/dist-packages/Keras-2.2.4-py2.7.egg/keras/engine/training.py", line 1047, in fit
validation_steps=validation_steps)
File "/usr/local/lib/python2.7/dist-packages/Keras-2.2.4-py2.7.egg/keras/engine/training_arrays.py", line 195, in fit_loop
outs = fit_function(ins_batch)
File "/usr/local/lib/python2.7/dist-packages/Keras-2.2.4-py2.7.egg/keras/backend/tensorflow_backend.py", line 2897, in __call__
return self._call(inputs)
File "/usr/local/lib/python2.7/dist-packages/Keras-2.2.4-py2.7.egg/keras/backend/tensorflow_backend.py", line 2855, in _call
fetched = self._callable_fn(*array_vals)
File "/home/asattar/.local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1439, in __call__
run_metadata_ptr)
File "/home/asattar/.local/lib/python2.7/site-packages/tensorflow/python/framework/errors_impl.py", line 528, in __exit__
c_api.TF_GetCode(self.status.status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [128,784] vs. [96,784]
[[{{node training/RMSprop/gradients/loss/dense_5_loss/logistic_loss/mul_grad/BroadcastGradientArgs}} = BroadcastGradientArgs[T=DT_INT32, _class=["loc:#train...ad/Reshape"], _device="/job:localhost/replica:0/task:0/device:CPU:0"](training/RMSprop/gradients/loss/dense_5_loss/logistic_loss/mul_grad/Shape, training/RMSprop/gradients/loss/dense_5_loss/logistic_loss/mul_grad/Shape_1)]]
Please note the "Incompatible shapes: [128,784] vs. [96,784]" in the stack trace" towards the end of the trace.

Answer 1

According to Keras: What if the size of data is not divisible by batch_size?, one should better use model.fit_generator rather than model.fit here.
To use model.fit_generator, one should define one's own generator object.
Following is an example:
from keras.utils import Sequence
import math
class Generator(Sequence):
# Class is a dataset wrapper for better training performance
def __init__(self, x_set, y_set, batch_size=256):
self.x, self.y = x_set, y_set
self.batch_size = batch_size
self.indices = np.arange(self.x.shape[0])
def __len__(self):
return math.floor(self.x.shape[0] / self.batch_size)
def __getitem__(self, idx):
inds = self.indices[idx * self.batch_size:(idx + 1) * self.batch_size]
batch_x = self.x[inds]
batch_y = self.y[inds]
return batch_x, batch_y
def on_epoch_end(self):
np.random.shuffle(self.indices)
train_datagen = Generator(x_train, x_train, batch_size)
test_datagen = Generator(x_test, x_test, batch_size)
vae.fit_generator(train_datagen,
steps_per_epoch=len(x_train)//batch_size,
validation_data=test_datagen,
validation_steps=len(x_test)//batch_size,
epochs=epochs)
Code adopted from How to shuffle after each epoch using a custom generator?.

Answer 2

Just tried to replicate and found out that when you define
x = Input(batch_shape=(batch_size, original_dim))
you're setting the batch size and it's causing a mismatch when it starts to validate. Change to
x = Input(shape=input_shape)
and you should be all set.