In this part the input must be prepared.
1 - The MNIST data will be downloaded.
2 - The images and labels for both training and testing will be extracted.
3 - The prepared data format(?,784) is different by the appropriate image shape(?,28,28,1) which needs
to be fed to the CNN architecture. So it needs to be reshaped.
'''
# Download and get MNIST dataset(available in tensorflow.contrib.learn.python.learn.datasets.mnist)
# It checks and download MNIST if it's not already downloaded then extract it.
# The 'reshape' is True by default to extract feature vectors but we set it to false to we get the original images.
mnist = input_data.read_data_sets("MNIST_data/", reshape=False, one_hot=False)
# The 'input.provide_data' is provided to organize any custom dataset which has specific characteristics.
data = input.provide_data(mnist)
# Dimentionality of train
dimensionality_train = data.train.images.shape
# Dimensions
num_train_samples = dimensionality_train[0]
height = dimensionality_train[1]
width = dimensionality_train[2]
num_channels = dimensionality_train[3]
#######################################
########## Defining Graph ############
#######################################
graph = tf.Graph()
In this part the input must be prepared.
1 - The MNIST data will be downloaded.
2 - The images and labels for both training and testing will be extracted.
3 - The prepared data format(?,784) is different by the appropriate image shape(?,28,28,1) which needs
to be fed to the CNN architecture. So it needs to be reshaped.
'''
# Download and get MNIST dataset(available in tensorflow.contrib.learn.python.learn.datasets.mnist)
# It checks and download MNIST if it's not already downloaded then extract it.
# The 'reshape' is True by default to extract feature vectors but we set it to false to we get the original images.
mnist = input_data.read_data_sets("MNIST_data/", reshape=False, one_hot=False)
# The 'input.provide_data' is provided to organize any custom dataset which has specific characteristics.
data = input.provide_data(mnist)
# Dimentionality of train
dimensionality_train = data.train.images.shape
# Dimensions
num_train_samples = dimensionality_train[0]
height = dimensionality_train[1]
width = dimensionality_train[2]
num_channels = dimensionality_train[3]
#######################################
########## Defining Graph ############
#######################################
graph = tf.Graph()
