image rec.py

# Tensorflow and numpy to create the neural network
import tensorflow as tf
import numpy as np

# Matplotlib to plot info to show our results
import matplotlib.pyplot as plt

# OS to load files and save checkpoints
import os

%matplotlib inline



# Load MNIST data from tf examples

image_height = 28
image_width = 28

color_channels = 1

model_name = "mnist"

mnist = tf.contrib.learn.datasets.load_dataset("mnist")

train_data = mnist.train.images
train_labels = np.asarray(mnist.train.labels, dtype=np.int32)

eval_data = mnist.test.images
eval_labels = np.asarray(mnist.test.labels, dtype=np.int32)

category_names = list(map(str, range(10)))

# TODO: Process mnist data

# Load cifar data from file

image_height = 32
image_width = 32

color_channels = 3

model_name = "cifar"


def unpickle(file):
    import pickle
    with open(file, 'rb') as fo:
        dict = pickle.load(fo, encoding='bytes')
    return dict


cifar_path = './cifar-10-data/'

train_data = np.array([])
train_labels = np.array([])

# Load all the data batches.
for i in range(1, 6):
    data_batch = unpickle(cifar_path + 'data_batch_' + str(i))
    train_data = np.append(train_data, data_batch[b'data'])
    train_labels = np.append(train_labels, data_batch[b'labels'])

# Load the eval batch.
eval_batch = unpickle(cifar_path + 'test_batch')

eval_data = eval_batch[b'data']
eval_labels = eval_batch[b'labels']

# Load the english category names.
category_names_bytes = unpickle(cifar_path + 'batches.meta')[b'label_names']
category_names = list(map(lambda x: x.decode("utf-8"), category_names_bytes))


# TODO: Process Cifar data

def process_data(data):
    float_data = np.array(data, dtype=float) / 255.0

    reshaped_data = np.reshape(float_data, (-1, color_channels, image_height, image_width))

    # The incorrect image

    transposed_data = np.transpose(reshaped_data, [0, 2, 3, 1])

    return transposed_data


train_data = process_data(train_data)

eval_data = process_data(eval_data)