examples/extending_tensorflow/layers.py

"""
This tutorial will introduce how to combine TFLearn and Tensorflow, using
TFLearn trainer with regular Tensorflow graph.
"""
from __future__ import print_function

import tensorflow.compat.v1 as tf
import tflearn

# --------------------------------------
# High-Level API: Using TFLearn wrappers
# --------------------------------------

# Using MNIST Dataset
import tflearn.datasets.mnist as mnist
mnist_data = mnist.read_data_sets(one_hot=True)

# User defined placeholders
with tf.Graph().as_default():
    # Placeholders for data and labels
    X = tf.placeholder(shape=(None, 784), dtype=tf.float32)
    Y = tf.placeholder(shape=(None, 10), dtype=tf.float32)

    net = tf.reshape(X, [-1, 28, 28, 1])

    # Using TFLearn wrappers for network building
    net = tflearn.conv_2d(net, 32, 3, activation='relu')
    net = tflearn.max_pool_2d(net, 2)
    net = tflearn.local_response_normalization(net)
    net = tflearn.dropout(net, 0.8)
    net = tflearn.conv_2d(net, 64, 3, activation='relu')
    net = tflearn.max_pool_2d(net, 2)
    net = tflearn.local_response_normalization(net)
    net = tflearn.dropout(net, 0.8)
    net = tflearn.fully_connected(net, 128, activation='tanh')
    net = tflearn.dropout(net, 0.8)
    net = tflearn.fully_connected(net, 256, activation='tanh')
    net = tflearn.dropout(net, 0.8)
    net = tflearn.fully_connected(net, 10, activation='linear')

    # Defining other ops using Tensorflow
    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=net, labels=Y))
    optimizer = tf.train.AdamOptimizer(learning_rate=0.01).minimize(loss)

    # Initializing the variables
    init = tf.global_variables_initializer()

    # Launch the graph
    with tf.Session() as sess:
        sess.run(init)

        batch_size = 128
        for epoch in range(2):  # 2 epochs
            avg_cost = 0.
            total_batch = int(mnist_data.train.num_examples / batch_size)
            for i in range(total_batch):
                batch_xs, batch_ys = mnist_data.train.next_batch(batch_size)
                sess.run(optimizer, feed_dict={X: batch_xs, Y: batch_ys})
                cost = sess.run(loss, feed_dict={X: batch_xs, Y: batch_ys})
                avg_cost += cost / total_batch
                if i % 20 == 0:
                    print("Epoch:", '%03d' % (epoch + 1), "Step:", '%03d' % i,
                          "Loss:", str(cost))