MNIST reads the data one-hot to create tansorboard data and stores the training results.
%autosave 0
def reset_graph(seed=42):
tf.reset_default_graph()
tf.set_random_seed(seed)
np.random.seed(seed)
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
import tensorflow as tf
import numpy as np
n_inputs = 28*28 # MNIST
n_hidden1 = 300
n_hidden2 = 100
n_hidden3 = 150
n_outputs = 10
logs_path = 'tensorboard_logs'
reset_graph()
X = tf.placeholder(tf.float32, shape=(None, n_inputs), name="X")
y = tf.placeholder(tf.int64, shape=(None, n_outputs), name="y")
with tf.name_scope("dnn"):
hidden1 = tf.layers.dense(inputs=X, units=n_hidden1, activation=tf.nn.relu)
hidden2 = tf.layers.dense(inputs=hidden1, units=n_hidden2, activation=tf.nn.relu)
hidden3 = tf.layers.dense(inputs=hidden2, units=n_hidden3, activation=tf.nn.relu)
logits = tf.layers.dense(inputs=hidden2, units=n_outputs)
with tf.name_scope("loss"):
xentropy = tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=logits)
loss = tf.reduce_mean(xentropy, name="loss")
learning_rate = 0.01
with tf.name_scope("train"):
optimizer = tf.train.GradientDescentOptimizer(learning_rate)
training_op = optimizer.minimize(loss)
with tf.name_scope("eval"):
correct = tf.nn.in_top_k(logits, tf.argmax(y,1), 1)
accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))
init = tf.global_variables_initializer()
saver = tf.train.Saver()
n_epochs = 40
batch_size = 50
tf.summary.scalar("loss", loss)
tf.summary.scalar("accuracy", accuracy)
merged_summary_op = tf.summary.merge_all()
with tf.Session() as sess:
init.run()
count = 0
summary_writer = tf.summary.FileWriter(logs_path, graph=tf.get_default_graph())
for epoch in range(n_epochs):
for iteration in range(mnist.train.num_examples // batch_size):
X_batch, y_batch = mnist.train.next_batch(batch_size)
_, summary = sess.run([training_op, merged_summary_op], feed_dict={X: X_batch, y: y_batch})
count += 1
summary_writer.add_summary(summary, global_step=count)
acc_train = accuracy.eval(feed_dict={X: X_batch, y: y_batch})
acc_test = accuracy.eval(feed_dict={X: mnist.test.images, y: mnist.test.labels})
print(epoch, "Train accuracy:", acc_train, "Test accuracy:", acc_test)
tf.add_to_collection("X", X)
tf.add_to_collection("logits", logits)
save_path = saver.save(sess, "./my_model_final.ckpt")
Reads the trained model and produces results.
import tensorflow as tf
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
tf.reset_default_graph()
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
saver = tf.train.import_meta_graph("./my_model_final.ckpt.meta")
saver.restore(sess, "./my_model_final.ckpt")
logits = tf.get_collection("logits")[0]
X = tf.get_collection("X")[0]
X_new_scaled = mnist.test.images[:20]
Z = logits.eval(feed_dict={X: X_new_scaled})
y_pred = np.argmax(Z, axis=1)
print("Predicted classes:", y_pred)
print("Actual classes: ", mnist.test.labels[:20])