import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets(".", one_hot=True)
INPUT_PARAMETERS = 784
L1_PARAMETERS = 300
W1 = tf.Variable(tf.truncated_normal([INPUT_PARAMETERS, L1_PARAMETERS], stddev=0.1))
b1 = tf.Variable(tf.truncated_normal([L1_PARAMETERS], stddev=0.1))
W2 = tf.Variable(tf.truncated_normal([L1_PARAMETERS, 10], stddev=0.1))
b2 = tf.Variable(tf.truncated_normal([10], stddev=0.1))
x = tf.placeholder(tf.float32, [None, INPUT_PARAMETERS])
y_ = tf.placeholder(tf.float32, [None, 10])
hidden1 = tf.nn.sigmoid(tf.matmul(x, W1) + b1)
y = tf.matmul(hidden1, W2) + b2
loss = (tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
logits=y, labels=y_)))
train_step = tf.train.GradientDescentOptimizer(0.3).minimize(loss)
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
loss_array = []
accuracy_array = []
with tf.Session() as sess:
tf.global_variables_initializer().run()
for i in range(10000):
batch_xs, batch_ys = mnist.train.next_batch(100)
_, step_loss = sess.run([train_step, loss], feed_dict={x: batch_xs, y_: batch_ys})
if i % 500 == 0:
step_accuracy = accuracy.eval({x: mnist.test.images, y_: mnist.test.labels})
loss_array.append(step_loss)
accuracy_array.append(step_accuracy)
print(step_loss, step_accuracy)
loss_array.append(step_loss)
accuracy_array.append(step_accuracy)
print(step_loss, step_accuracy)
plt.plot([i*500 for i in range(len(loss_array))], loss_array, 'b-',
[i*500 for i in range(len(accuracy_array))], accuracy_array, 'r-')
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