Tensorflow: Convolutional Neural Networks in Tensorflow(without Keras)
This video gives a detail of dealing with CNN using Tensorflow without using keras
Code:
import tensorflow as tf
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
mnist=input_data.read_data_sets("MNIST_data/",one_hot=True)
learning_rate = 0.0001
epochs = 2
batch_size = 50
x = tf.placeholder(tf.float32, [None, 784])
x_shaped = tf.reshape(x, [-1, 28, 28, 1])
y = tf.placeholder(tf.float32, [None, 10])
def create_new_conv_layer(input_data, num_input_channels, num_filters, filter_shape, pool_shape, name):
conv_filt_shape = [filter_shape[0], filter_shape[1], num_input_channels,
num_filters]
weights = tf.Variable(tf.truncated_normal(conv_filt_shape, stddev=0.03),
name=name+'_W')
bias = tf.Variable(tf.truncated_normal([num_filters]), name=name+'_b')
out_layer = tf.nn.conv2d(input_data, weights, [1, 1, 1, 1], padding='SAME')
out_layer += bias
out_layer = tf.nn.relu(out_layer)
ksize = [1, pool_shape[0], pool_shape[1], 1]
strides = [1, 2, 2, 1]
out_layer = tf.nn.max_pool(out_layer, ksize=ksize, strides=strides,
padding='SAME')
return out_layer
layer1 = create_new_conv_layer(x_shaped, 1, 32, [5, 5], [2, 2], name='layer1')
layer2 = create_new_conv_layer(layer1, 32, 64, [5, 5], [2, 2], name='layer2')
flattened = tf.reshape(layer2, [-1, 7 * 7 * 64])
wd1 = tf.Variable(tf.truncated_normal([7 * 7 * 64, 1000], stddev=0.03), name='wd1')
bd1 = tf.Variable(tf.truncated_normal([1000], stddev=0.01), name='bd1')
dense_layer1 = tf.matmul(flattened, wd1) + bd1
dense_layer1 = tf.nn.relu(dense_layer1)
wd2 = tf.Variable(tf.truncated_normal([1000, 10], stddev=0.03), name='wd2')
bd2 = tf.Variable(tf.truncated_normal([10], stddev=0.01), name='bd2')
dense_layer2 = tf.matmul(dense_layer1, wd2) + bd2
y_ = tf.nn.softmax(dense_layer2)
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=dense_layer2, labels=y))
optimiser = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
init_op = tf.global_variables_initializer()
tf.summary.scalar('accuracy', accuracy)
merged = tf.summary.merge_all()
writer = tf.summary.FileWriter('TensorFlowProject')
with tf.Session() as sess:
sess.run(init_op)
total_batch = int(len(mnist.train.labels) / batch_size)
for epoch in range(epochs):
print('epoch no: ',epoch)
avg_cost = 0
for i in range(total_batch):
batch_x, batch_y = mnist.train.next_batch(batch_size=batch_size)
_, c = sess.run([optimiser, cross_entropy], feed_dict={x: batch_x, y: batch_y})
avg_cost += c / total_batch
test_acc = sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels})
summary = sess.run(merged, feed_dict={x: mnist.test.images, y: mnist.test.labels})
writer.add_summary(summary, epoch)
print("\nTraining complete!")
writer.add_graph(sess.graph)
print(sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels}))
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