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import os
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
DATA_DIR = 'tmp/data'
NUM_STEPS = 1000
MINIBATCH_SIZE = 100
LEARNING_RATE = 0.5
DIR = "model"
# Layers sizes
L1 = 200
L2 = 100
L3 = 60
L4 = 30
L5 = 10
data = input_data.read_data_sets(DATA_DIR , one_hot=True)
x = tf.placeholder(tf.float32, [None, 784])
l1 = tf.layers.dense(x,L1,activation=tf.nn.relu,use_bias=True)
l2 = tf.layers.dense(l1,L2,activation=tf.nn.relu,use_bias=True)
l3 = tf.layers.dense(l2,L3,activation=tf.nn.relu,use_bias=True)
l4 = tf.layers.dense(l3,L4,activation=tf.nn.relu,use_bias=True)
y_pred = tf.layers.dense(l4,L5,activation=tf.nn.relu,use_bias=True)
y_true = tf.placeholder(tf.float32, [None, 10])
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits( logits=y_pred, labels=y_true))
gd_step = tf.train.GradientDescentOptimizer(LEARNING_RATE).minimize(cross_entropy)
correct_mask = tf.equal(tf.argmax(y_pred , 1), tf.argmax(y_true , 1))
accuracy = tf.reduce_mean(tf.cast(correct_mask , tf.float32))
saver = tf.train.Saver(max_to_keep=7, keep_checkpoint_every_n_hours=1)
with tf.Session() as sess:
# Тренируем
sess.run(tf.global_variables_initializer())
for step in range(1,NUM_STEPS+1):
batch_x , batch_y = data.train.next_batch(MINIBATCH_SIZE)
sess.run(gd_step, feed_dict={x:batch_x,y_true:batch_y})
# Сохраняем модель каждую 50 итерацию
if step % 50 == 0:
saver.save(sess, os.path.join(DIR, "model_ckpt"), global_step=step)
ans = sess.run(accuracy, feed_dict={x: data.test.images, y_true: data.test.labels})
print("Accuracy: {:.4}%".format(ans*100))
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