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问题:

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.

回答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?.