I am new in machine learning and using following code to create a sample VAE on MNISET dataset
# We are going to use MINISET Dataset to train our GAN.
# All imports are going to be in this place.
import numpy as np
# Progressbar
from keras.callbacks import TensorBoard
from tqdm import tqdm
import matplotlib.pyplot as plt
from keras.datasets import mnist
from keras.layers import Conv2D
from keras.models import Model
from keras.layers import Input
from keras.layers import Dense
from keras.layers import LeakyReLU
from keras.layers import Dropout
from keras.layers import MaxPooling2D
from keras.layers import BatchNormalization
from keras.layers import Flatten
# All Defs will be defined here
# Importing training data of MINISET
def loadData():
# y_train contains the labels, numbers are 1, 2 or 5, 7 etc.
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# Normalizing based on color 255
x_train = (x_train.astype(np.float32) - 127.5) / 127.5
# convert shape of x_train from (60000, 28, 28) to (60000, 784)
# 784 columns per row
# x_train = x_train.reshape(60000, 784)
return (x_train, y_train, x_test, y_test)
def getEncoder():
# This returns a tensor
inputs = Input(shape=(28, 28, 1))
# Making a sample AlexNet Model Layer 1
encoder = Conv2D(392, (4, 4), padding='same', activation='relu')(inputs)
encoder = MaxPooling2D((4, 4), strides=(1, 1), padding='same')(encoder)
encoder = BatchNormalization()(encoder)
# Making a sample AlexNet Model Layer 2
encoder = Conv2D(196, (2, 2), padding='same', activation='relu')(encoder)
encoder = MaxPooling2D((4, 4), strides=(1, 1), padding='same')(encoder)
encoder = BatchNormalization()(encoder)
# Making a sample AlexNet Model Layer 3
encoder = Conv2D(98, (2, 2), padding='same', activation='relu')(encoder)
encoder = MaxPooling2D((2, 2), strides=(1, 1), padding='same')(encoder)
encoder = BatchNormalization()(encoder)
#encoder = Flatten()(encoder)
#encoder = Dense(2)(encoder)
#encoder = Dropout(0.4)(encoder)
model = Model(inputs=inputs, outputs=encoder)
model.compile(optimizer='adam', loss='binary_crossentropy')
# model.fit(data, labels) # starts training
print(model.summary())
return model;
def getDecoder():
# This returns a tensor
inputs = Input(shape=(98,))
# a layer instance is callable on a tensor, and returns a tensor
disc = Dense(196)(inputs)
disc = LeakyReLU(alpha=0.2)(disc)
disc = Dropout(0.3)(disc)
disc = Dense(392)(disc)
disc = LeakyReLU(alpha=0.2)(disc)
disc = Dropout(0.3)(disc)
disc = Dense(784, activation='sigmoid')(disc)
disc = LeakyReLU(alpha=0.2)(disc)
model = Model(inputs=inputs, outputs=disc)
model.compile(optimizer='rmsprop', loss='binary_crossentropy')
# starts training
# model.fit(data, labels)
print(model.summary())
return model;
def createVAE(decoder, encoder):
inputs = Input(shape=(28, 28, 1))
gen = encoder(inputs)
output = decoder(gen)
vae = Model(inputs=inputs, outputs=output)
print(vae.summary())
vae.compile(loss='binary_crossentropy', optimizer='rmsprop')
return vae
def doTraining(epochs=1, batchSize=128):
# Loading the data
(mniTrainX, mniTrainY, mniTestX, mniTestY) = loadData()
# Creating GAN
encoder = getEncoder()
decoder = getDecoder()
vae = createVAE(decoder, encoder)
vae.fit(mniTrainX, mniTrainX,
epochs=epochs,
batch_size=batchSize,
shuffle=True,
validation_data=(mniTestX, mniTestX),
callbacks = [TensorBoard(log_dir='/tmp/autoencoder')])
doTraining(10, 128)
The model summary of VAE is
None
Model: "model_3"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_3 (InputLayer) (None, 28, 28, 1) 0
_________________________________________________________________
model_1 (Model) (None, 28, 28, 98) 393862
_________________________________________________________________
model_2 (Model) multiple 404740
=================================================================
Total params: 798,602
Trainable params: 797,230
Non-trainable params: 1,372
Now its giving error
ValueError: Error when checking input: expected input_3 to have 4 dimensions, but got array with shape (60000, 28, 28)
What am I missing here, whats the 4 dimension.
Your problem is in loadData. Last line needs to be changed as follows. Your encoder expects an image that has one channel, but your current data don't have that channel.
return (np.expand_dims(x_train, -1), y_train, np.expand_dims(x_test, -1), y_test)
However, you'll run into more issues.
For example, your encoder outputs the following.
batch_normalization_3 (Batch (None, 28, 28, 98) 392
But your decoder takes the following input
input_2 (InputLayer) (None, 98)
Then you're trying to send the output of the encoder ( which is None, 28, 28, 98) to a (None, 98) input layer. Which will not work.
Warning: If you are just starting with TF/Keras, Convolutional VAE is a lot to take in. I suggest going with simpler models like, image classification / autoencoder , etc models rather than jumping to a complex model like a convolutional VAE. But if you're comfortable with this that's okay.
from tensorflow.keras.layers import Dense, Input, Conv2D, Conv2DTranspose, MaxPooling2D, LeakyReLU, Reshape, BatchNormalization, Flatten, Lambda
from tensorflow.keras.models import Model
import tensorflow.keras.backend as K
from tensorflow.keras.datasets import mnist
import numpy as np
K.clear_session()
def loadData():
# y_train contains the labels, numbers are 1, 2 or 5, 7 etc.
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# Normalizing based on color 255
x_train = x_train.astype(np.float32) /255.0
x_test = x_test.astype(np.float32)/255.0
# convert shape of x_train from (60000, 28, 28) to (60000, 784)
# 784 columns per row
# x_train = x_train.reshape(60000, 784)
return (np.expand_dims(x_train, axis=-1), y_train, np.expand_dims(x_test, axis=-1), y_test)
# use reparameterization trick to push the sampling out as input
# note that "output_shape" isn't necessary with the TensorFlow backend
def sampling(args):
"""Reparameterization trick by sampling from an isotropic unit Gaussian.
# Arguments
args (tensor): mean and log of variance of Q(z|X)
# Returns
z (tensor): sampled latent vector
"""
z_mean, z_log_var = args
batch = K.shape(z_mean)[0]
dim = K.int_shape(z_mean)[1]
# by default, random_normal has mean = 0 and std = 1.0
epsilon = K.random_normal(shape=(batch, dim))
return z_mean + K.exp(0.5 * z_log_var) * epsilon
def getEncoder():
# This returns a tensor
inputs = Input(shape=(28, 28, 1))
# Making a sample AlexNet Model Layer 1
encoder = Conv2D(32, (4, 4), padding='same', activation='relu')(inputs)
encoder = MaxPooling2D((4, 4), strides=(2, 2), padding='same')(encoder) # 14,14
encoder = BatchNormalization()(encoder)
# Making a sample AlexNet Model Layer 2
encoder = Conv2D(64, (2, 2), padding='same', activation='relu')(encoder)
encoder = MaxPooling2D((4, 4), strides=(2, 2), padding='same')(encoder) # 7, 4
encoder = BatchNormalization()(encoder)
latent_dim = 64
encoder = Flatten()(encoder)
z_mean = Dense(latent_dim, name='z_mean')(encoder)
z_log_var = Dense(latent_dim, name='z_log_var')(encoder)
# This is to perform the reparametrization trick
z = Lambda(sampling, output_shape=(latent_dim,), name='z')([z_mean, z_log_var])
encoder = Model(inputs=inputs, outputs=[z_mean, z_log_var, z])
#encoder.summary()
return encoder
def getDecoder():
# This returns a tensor of shape (None, 28, 28, 1) exact same shape as input
latent_dim = 64
inputs = Input(shape=(latent_dim,))
disc = Dense(7*7*1)(inputs)
disc = LeakyReLU(alpha=0.2)(disc)
disc = Reshape([7,7,1])(disc)
# a layer instance is callable on a tensor, and returns a tensor
disc = Conv2DTranspose(32, (3,3), strides=(2,2), padding='same', activation='relu')(disc)
disc = Conv2DTranspose(1, (3,3), strides=(2,2), padding='same', activation='sigmoid')(disc)
decoder = Model(inputs=inputs, outputs=disc)
#decoder.summary()
return decoder
def kl_loss(z_mean, z_log_var):
kl_loss = 1 + z_log_var - K.square(z_mean) - K.exp(z_log_var)
kl_loss = K.sum(kl_loss, axis=-1)
kl_loss *= -0.5
return K.mean(kl_loss)
def createVAE(decoder, encoder):
# We are saying that the decoder takes the last output of the encoder as the input
dec_out = decoder(encoder.outputs[2])
# Defining an end-to-end model with encoder inputs and decoder outputs
vae = Model(inputs=encoder.inputs, outputs=dec_out)
print(vae.summary())
# VAE loss comprises both crossentropy and KL divergence loss
vae.compile(loss='binary_crossentropy', optimizer='rmsprop')
vae.add_loss(kl_loss(encoder.outputs[0], encoder.outputs[1]))
return vae
def doTraining(epochs=1, batchSize=128):
# Loading the data
(mniTrainX, mniTrainY, mniTestX, mniTestY) = loadData()
# Creating GAN
encoder = getEncoder()
decoder = getDecoder()
vae = createVAE(decoder, encoder)
# I have removed the tensorboard callback. If needed add that.
vae.fit(mniTrainX, mniTrainX,
epochs=epochs,
batch_size=batchSize,
shuffle=True,
validation_data=(mniTestX, mniTestX)
)
encoder = getEncoder()
decoder = getDecoder()
vae = createVAE(decoder, encoder)
doTraining()