CNN_3d.py

import tensorflow as tf
import numpy as np
import urllib.request


IMG_SIZE_PX = 50
SLICE_COUNT = 20
n_classes = 2

x = tf.placeholder("float")
y = tf.placeholder("float")

keep_rate = 0.8
keep_prob = tf.placeholder(tf.float32 )

def conv3d(x, W):
    return tf.nn.conv3d(x, W, strides=[1,1,1,1,1], padding='SAME')

def maxpool3d(x):
    return tf.nn.max_pool3d(x, ksize  =[1,2,2,2,1], strides=[1,2,2,2,1], padding='SAME')

def convolutional_neural_network(x):

    weights = {'W_conv1': tf.Variable(tf.random_normal([3,3,3,1,32])),
               'W_conv2': tf.Variable(tf.random_normal([3,3,3,32,64])),
               'W_fc': tf.Variable(tf.random_normal([54080, 1024])),
               'out': tf.Variable(tf.random_normal([1024, n_classes]))}

    biases = {'b_conv1': tf.Variable(tf.random_normal([32])),
               'b_conv2': tf.Variable(tf.random_normal([64])),
               'b_fc': tf.Variable(tf.random_normal([1024])),
               'out': tf.Variable(tf.random_normal([n_classes]))}

    x = tf.reshape(x, shape=[-1, IMG_SIZE_PX, IMG_SIZE_PX, SLICE_COUNT, 1])

    conv1 = tf.nn.relu(conv3d(x, weights['W_conv1']) + biases['b_conv1'])
    conv1 = maxpool3d(conv1)

    conv2 = tf.nn.relu(conv3d(conv1, weights['W_conv2']) + biases['b_conv2'])
    conv2 = maxpool3d(conv2)

    fc = tf.reshape(conv2, [-1, 54080])
    fc = tf.nn.relu(tf.matmul(fc, weights['W_fc']) + biases['b_fc'])

    fc = tf.nn.dropout(fc, keep_rate)

    output = tf.matmul(fc, weights['out']) + biases['out']

    return output

def train_neural_network(x):
    myurl = 'https://s3.amazonaws.com/cse6250-nliu71/alldata1-50-50-20.npy'
    much_data = urllib.request.urlopen(myurl).read()
    # much_data = np.load(myfile)
    train_data = much_data[:-20]
    validation_data = much_data[-20:]


    prediction = convolutional_neural_network(x)
    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y))

    # learning_rate = 0.001
    optimizer = tf.train.AdamOptimizer().minimize(cost)

    hm_epochs = 10

    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())

        # train the samples
        for epoch in range(hm_epochs):
            epoch_loss = 0
            success_total = 0
            attempt_total = 0
            for data in train_data:
                attempt_total += 1
                try:
                    X = data[0]
                    Y = data[1]
                    _, c = sess.run([optimizer, cost], feed_dict = {x: X, y: Y})
                    epoch_loss += c
                    success_total += 1
                except Exception as e:
                    pass
            print('Epoch', epoch, 'completed out of', hm_epochs, 'loss:', epoch_loss, 'success rate:',success_total/attempt_total)

        correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))
        accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
        print('Accuracy:', accuracy.eval({x:[i[0] for i in validation_data], y:[i[1] for i in validation_data]}))


train_neural_network(x)