UIS project-2 documentation

codes/3-neural_networks/recurrent-neural-networks/code/rnn.py

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+'''
+MNIST using Recurrent Neural Network to predict handwritten digits
+In this tutorial, I am going to demonstrate how to use recurrent neural 
+network to predict the famous handwritten digits "MNIST".
+The MNIST dataset consists:
+mnist.train: 55000 training images
+mnist.validation: 5000 validation images
+mnist.test: 10000 test images
+Each image is 28 pixels (rows) by 28 pixels (cols).
+'''
+
+import tensorflow as tf
+import numpy as np
+import matplotlib.pyplot as plt
+import argparse
+
+# Useful function for arguments.
+def str2bool(v):
+    return v.lower() in ("yes", "true")
+
+# Parser
+parser = argparse.ArgumentParser(description='Creating Classifier')
+
+######################
+# Optimization Flags #
+######################
+
+parser.add_argument('--learning_rate', default=0.001, type=float, help='initial learning rate')
+parser.add_argument('--seed', default=111, type=int, help='seed')
+
+##################
+# Training Flags #
+##################
+parser.add_argument('--batch_size', default=128, type=int, help='Batch size for training')
+parser.add_argument('--num_epoch', default=10, type=int, help='Number of training iterations')
+parser.add_argument('--batch_per_log', default=10, type=int, help='Print the log at what number of batches?')
+
+###############
+# Model Flags #
+###############
+parser.add_argument('--hidden_size', default=128, type=int, help='Number of neurons for RNN hodden layer')
+
+# Add all arguments to parser
+args = parser.parse_args()
+
+
+# Reset the graph set the random numbers to be the same using "seed"
+tf.reset_default_graph()
+tf.set_random_seed(args.seed)
+np.random.seed(args.seed)
+
+# Divide 28x28 images to rows of data to feed to RNN as sequantial information
+step_size = 28
+input_size = 28
+output_size = 10
+
+# Input tensors
+X = tf.placeholder(tf.float32, [None, step_size, input_size])
+y = tf.placeholder(tf.int32, [None])
+
+# Rnn
+cell = tf.nn.rnn_cell.BasicRNNCell(num_units=args.hidden_size)
+output, state = tf.nn.dynamic_rnn(cell, X, dtype=tf.float32)
+
+# Forward pass and loss calcualtion
+logits = tf.layers.dense(state, output_size)
+cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)
+loss = tf.reduce_mean(cross_entropy)
+
+# optimizer
+optimizer = tf.train.AdamOptimizer(learning_rate=args.learning_rate).minimize(loss)
+
+# Prediction
+prediction = tf.nn.in_top_k(logits, y, 1)
+accuracy = tf.reduce_mean(tf.cast(prediction, tf.float32))
+
+# input data
+from tensorflow.examples.tutorials.mnist import input_data
+mnist = input_data.read_data_sets("MNIST_data/")
+
+# Process MNIST
+X_test = mnist.test.images # X_test shape: [num_test, 28*28]
+X_test = X_test.reshape([-1, step_size, input_size])
+y_test = mnist.test.labels
+
+# initialize the variables
+init = tf.global_variables_initializer()
+
+# Empty list for tracking
+loss_train_list = []
+acc_train_list = []
+
+# train the model
+with tf.Session() as sess:
+    sess.run(init)
+    n_batches = mnist.train.num_examples // args.batch_size
+    for epoch in range(args.num_epoch):
+        for batch in range(n_batches):
+            X_train, y_train = mnist.train.next_batch(args.batch_size)
+            X_train = X_train.reshape([-1, step_size, input_size])
+            sess.run(optimizer, feed_dict={X: X_train, y: y_train})
+        loss_train, acc_train = sess.run(
+            [loss, accuracy], feed_dict={X: X_train, y: y_train})
+        loss_train_list.append(loss_train)
+        acc_train_list.append(acc_train)
+        print('Epoch: {}, Train Loss: {:.3f}, Train Acc: {:.3f}'.format(
+            epoch + 1, loss_train, acc_train))
+    loss_test, acc_test = sess.run(
+        [loss, accuracy], feed_dict={X: X_test, y: y_test})
+    print('Test Loss: {:.3f}, Test Acc: {:.3f}'.format(loss_test, acc_test))