tfdemo.py

# Freely adapted from Aymeric Damien's code
# This code is attributed to original author with modifications to load Tamil MNIST like data.
""" Neural Network.
A 2-Hidden Layers Fully Connected Neural Network (a.k.a Multilayer Perceptron)
implementation with TensorFlow. This example is using the MNIST database
of handwritten digits (http://yann.lecun.com/exdb/mnist/).
Links:
    [MNIST Dataset](http://yann.lecun.com/exdb/mnist/).
Author: Aymeric Damien
Project: https://github.com/aymericdamien/TensorFlow-Examples/

"""

import os
import numpy as np

import tensorflow as tf
#from tensorflow import keras


def load_mnist_data(mode='train'):
    path = os.path.split(__file__)[0]
    labels_path = os.path.join(path,'data',mode+'-label-onehot.npy')
    images_path = os.path.join(path,'data',mode+'-image.npy')
    labels = np.load(labels_path)
    images = np.load(images_path)
    return labels,images

# build model
batch_size = 128
num_classes = 13
epochs = 12

# input image dimensions
img_rows, img_cols = 28, 28

labels,images = load_mnist_data('train')
test_labels,test_images = load_mnist_data('test')
#images=images.squeeze()
offset = 60000
x_train = images[0:offset,:]
y_train = labels[0:offset,:]
offset=0
x_test = test_images[offset:,:]
y_test = test_labels[offset:,:]

print('deep dive!')

# if K.image_data_format() == 'channels_first':
#     x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
#     x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
#     input_shape = (1, img_rows, img_cols)
# else:
#     x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
#     x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
#     input_shape = (img_rows, img_cols, 1)
input_shape=(img_rows*img_cols,)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

# Parameters
learning_rate = 0.075
num_steps = 150
batch_size = 128
display_step = 10

# Network Parameters
n_hidden_1 = 256 # 1st layer number of neurons
n_hidden_2 = 256 # 2nd layer number of neurons
num_input = 784 # MNIST data input (img shape: 28*28)
num_classes = 13 # MNIST total classes (0-9 digits)

# tf Graph input
X = tf.placeholder("float", [None, num_input])
Y = tf.placeholder("float", [None, num_classes])

# Store layers weight & bias
weights = {
    'h1': tf.Variable(tf.random_normal([num_input, n_hidden_1])),
    'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
    'out': tf.Variable(tf.random_normal([n_hidden_2, num_classes]))
}
biases = {
    'b1': tf.Variable(tf.random_normal([n_hidden_1])),
    'b2': tf.Variable(tf.random_normal([n_hidden_2])),
    'out': tf.Variable(tf.random_normal([num_classes]))
}
# Create model
def neural_net(x):
    # Hidden fully connected layer with 256 neurons
    layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
    # Hidden fully connected layer with 256 neurons
    layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])
    # Output fully connected layer with a neuron for each class
    out_layer = tf.matmul(layer_2, weights['out']) + biases['out']
    return out_layer

# Construct model
logits = neural_net(X)
prediction = tf.nn.softmax(logits)

# Define loss and optimizer
loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
    logits=logits, labels=Y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
train_op = optimizer.minimize(loss_op)

# Evaluate model
correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

# Initialize the variables (i.e. assign their default value)
init = tf.global_variables_initializer()
filename = os.path.abspath(__file__)
#print(os.path.split(filename))
basedir =  os.path.split(filename)[0]
model_name = 'tamil_model_ckpt'
model_path = os.path.join(basedir,'tamil_model_ckpt',model_name)
export_dir =  os.path.join(basedir,  'exported' )
# Start training
with tf.Session() as sess:

    # Run the initializer
    sess.run(init)

    for step in range(1, num_steps+1):
        batch_x, batch_y = x_train[(step-1)*batch_size+1:step*batch_size,:],\
        y_train[(step-1)*batch_size+1:step*batch_size,:]
        # Run optimization op (backprop)
        sess.run(train_op, feed_dict={X: batch_x, Y: batch_y})
        if step % display_step == 0 or step == 1:
            # Calculate batch loss and accuracy
            loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x,
                                                                 Y: batch_y})
            print("Step " + str(step) + ", Minibatch Loss= " + \
                  "{:.4f}".format(loss) + ", Training Accuracy= " + \
                  "{:.3f}".format(acc))

    print("Optimization Finished!")

    # Calculate accuracy for MNIST test images
    print("Testing Accuracy:", \
        sess.run(accuracy, feed_dict={X: x_test,
                                      Y: y_test}))

    # save the model to disk
    saver= tf.train.Saver()
    saver.save(sess,model_path)