<reponame>tolkien/misc

# coding: utf-8

# MNIST with tensorboard, copied from lab7-2
import tensorflow as tf
import random
tf.set_random_seed(777)  # for reproducibility

from tensorflow.examples.tutorials.mnist import input_data
# Check out https://www.tensorflow.org/get_started/mnist/beginners for
# more information about the mnist dataset
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

nb_classes = 10

# MNIST data image of shape 28 * 28 = 784
X = tf.placeholder(tf.float32, [None, 784], name='x-input')
# 0 - 9 digits recognition = 10 classes
Y = tf.placeholder(tf.float32, [None, nb_classes], name='y-input')

with tf.name_scope("layer1") as scope:
    W = tf.Variable(tf.random_normal([784, nb_classes]))
    b = tf.Variable(tf.random_normal([nb_classes]))

    # Hypothesis (using softmax)
    hypothesis = tf.nn.softmax(tf.matmul(X, W) + b)
    
    W_hist = tf.summary.histogram("weight", W)
    b_hist = tf.summary.histogram("bias", b)
    hypothesis_hist = tf.summary.histogram("hypothesis", hypothesis)

with tf.name_scope("cost") as scope:
    cost = tf.reduce_mean(-tf.reduce_sum(Y * tf.log(hypothesis), axis=1))
    cost_summ = tf.summary.scalar("cost", cost)
with tf.name_scope("optimizer") as scope:
    optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(cost)

# Test model
is_correct = tf.equal(tf.argmax(hypothesis, 1), tf.argmax(Y, 1))
# Calculate accuracy
accuracy = tf.reduce_mean(tf.cast(is_correct, tf.float32))
accuracy_hist = tf.summary.scalar("accuracy", accuracy)

# tensorboard --logdir=./logs
merged_summary = tf.summary.merge_all()
writer = tf.summary.FileWriter("./logs/xor_logs_r9_02")

# parameters
training_epochs = 15
batch_size = 100

with tf.Session() as sess:
    # Initialize TensorFlow variables
    sess.run(tf.global_variables_initializer())
    writer.add_graph(sess.graph)  # Show the graph

    # Training cycle
    for epoch in range(training_epochs):
        avg_cost = 0
        total_batch = int(mnist.train.num_examples / batch_size)

        for i in range(total_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            summ, c, _ = sess.run([merged_summary, cost, optimizer], feed_dict={
                            X: batch_xs, Y: batch_ys})
            avg_cost += c / total_batch
            writer.add_summary(summ, global_step=epoch*total_batch + i)

        print('Epoch:', '%04d' % (epoch + 1),
              'cost =', '{:.9f}'.format(avg_cost))

    print("Learning finished")

    # Test the model using test sets
    print("Accuracy: ", accuracy.eval(session=sess, feed_dict={
          X: mnist.test.images, Y: mnist.test.labels}))

    # Get one and predict
    r = random.randint(0, mnist.test.num_examples - 1)
    print("Label: ", sess.run(tf.argmax(mnist.test.labels[r:r + 1], 1)))
    print("Prediction: ", sess.run(
        tf.argmax(hypothesis, 1), feed_dict={X: mnist.test.images[r:r + 1]}))

# coding: utf-8

# MNIST with tensorboard, copied from lab7-2
import tensorflow as tf
import random
tf.set_random_seed(777)  # for reproducibility

from tensorflow.examples.tutorials.mnist import input_data
# Check out https://www.tensorflow.org/get_started/mnist/beginners for
# more information about the mnist dataset
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

nb_classes = 10

# MNIST data image of shape 28 * 28 = 784
X = tf.placeholder(tf.float32, [None, 784], name='x-input')
# 0 - 9 digits recognition = 10 classes
Y = tf.placeholder(tf.float32, [None, nb_classes], name='y-input')

with tf.name_scope("layer1") as scope:
    W = tf.Variable(tf.random_normal([784, nb_classes]))
    b = tf.Variable(tf.random_normal([nb_classes]))

    # Hypothesis (using softmax)
    hypothesis = tf.nn.softmax(tf.matmul(X, W) + b)
    
    W_hist = tf.summary.histogram("weight", W)
    b_hist = tf.summary.histogram("bias", b)
    hypothesis_hist = tf.summary.histogram("hypothesis", hypothesis)

with tf.name_scope("cost") as scope:
    cost = tf.reduce_mean(-tf.reduce_sum(Y * tf.log(hypothesis), axis=1))
    cost_summ = tf.summary.scalar("cost", cost)
with tf.name_scope("optimizer") as scope:
    optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(cost)

# Test model
is_correct = tf.equal(tf.argmax(hypothesis, 1), tf.argmax(Y, 1))
# Calculate accuracy
accuracy = tf.reduce_mean(tf.cast(is_correct, tf.float32))
accuracy_hist = tf.summary.scalar("accuracy", accuracy)

# tensorboard --logdir=./logs
merged_summary = tf.summary.merge_all()
writer = tf.summary.FileWriter("./logs/xor_logs_r9_02")

# parameters
training_epochs = 15
batch_size = 100

with tf.Session() as sess:
    # Initialize TensorFlow variables
    sess.run(tf.global_variables_initializer())
    writer.add_graph(sess.graph)  # Show the graph

    # Training cycle
    for epoch in range(training_epochs):
        avg_cost = 0
        total_batch = int(mnist.train.num_examples / batch_size)

        for i in range(total_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            summ, c, _ = sess.run([merged_summary, cost, optimizer], feed_dict={
                            X: batch_xs, Y: batch_ys})
            avg_cost += c / total_batch
            writer.add_summary(summ, global_step=epoch*total_batch + i)

        print('Epoch:', '%04d' % (epoch + 1),
              'cost =', '{:.9f}'.format(avg_cost))

    print("Learning finished")

    # Test the model using test sets
    print("Accuracy: ", accuracy.eval(session=sess, feed_dict={
          X: mnist.test.images, Y: mnist.test.labels}))

    # Get one and predict
    r = random.randint(0, mnist.test.num_examples - 1)
    print("Label: ", sess.run(tf.argmax(mnist.test.labels[r:r + 1], 1)))
    print("Prediction: ", sess.run(
        tf.argmax(hypothesis, 1), feed_dict={X: mnist.test.images[r:r + 1]}))
# coding: utf-8 # MNIST with tensorboard, copied from lab7-2 # for reproducibility # Check out https://www.tensorflow.org/get_started/mnist/beginners for # more information about the mnist dataset # MNIST data image of shape 28 * 28 = 784 # 0 - 9 digits recognition = 10 classes # Hypothesis (using softmax) # Test model # Calculate accuracy # tensorboard --logdir=./logs # parameters # Initialize TensorFlow variables # Show the graph # Training cycle # Test the model using test sets # Get one and predict