amirhfarzaneh

import torchvision.datasets as datasets
import torchvision.transforms as transforms
import torch
import torchvision
import matplotlib.pyplot as plt
import numpy as np

from custom_transforms import NRandomCrop

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import numbers
import random
from torchvision.transforms import functional as F
try:
    import accimage
except ImportError:
    accimage = None
from PIL import Image

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eval_input_fn = tf.estimator.inputs.numpy_input_fn(x={"x": eval_data},
                                                   y=eval_labels,
                                                   num_epochs=1,
                                                   shuffle=False)
eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn)
print(eval_results)

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mnist_classifier.train(input_fn=train_input_fn,
                       steps=None,
                       hooks=None)

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train_input_fn = tf.estimator.inputs.numpy_input_fn(x={"x": train_data},
                                                        y=train_labels,
                                                        batch_size=100,
                                                        num_epochs=100,
                                                        shuffle=True)

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mnist_classifier = tf.estimator.Estimator(model_fn=cnn_model_fn,
                                          model_dir="/tmp/mnist_vgg13_model")

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def cnn_model_fn(features, labels, mode):
    # Input Layer
    input_height, input_width = 28, 28
    input_channels = 1
    input_layer = tf.reshape(features["x"], [-1, input_height, input_width, input_channels])

    # Convolutional Layer #1 and Pooling Layer #1
    conv1_1 = tf.layers.conv2d(inputs=input_layer, filters=64, kernel_size=[3, 3], padding="same", activation=tf.nn.relu)
    conv1_2 = tf.layers.conv2d(inputs=conv1_1, filters=64, kernel_size=[3, 3], padding="same", activation=tf.nn.relu)
    pool1 = tf.layers.max_pooling2d(inputs=conv1_2, pool_size=[2, 2], strides=2, padding="same")

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print ("number of training examples = " + str(train_data.shape[0]))
print ("number of evaluation examples = " + str(eval_data.shape[0]))
print ("X_train shape: " + str(train_data.shape))
print ("Y_train shape: " + str(train_labels.shape))
print ("X_test shape: " + str(eval_data.shape))
print ("Y_test shape: " + str(eval_labels.shape))

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index = 7
plt.imshow(train_data[index].reshape(28, 28))
print ("y = " + str(np.squeeze(train_labels[index])))

amirhfarzaneh

# Loading the data (MNIST)
mnist = tf.contrib.learn.datasets.load_dataset("mnist")
train_data = mnist.train.images  # Returns np.array
train_labels = np.asarray(mnist.train.labels, dtype=np.int32)
eval_data = mnist.test.images  # Returns np.array
eval_labels = np.asarray(mnist.test.labels, dtype=np.int32)