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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]))) |
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# 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) |
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