main1-CNN.py

import tensorflow as tf
import numpy as np
from CNN import CNN
from AutoEncoder import AutoEncoder
from ReadDataset import read
np.set_printoptions(threshold=np.nan)

config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.gpu_options.allocator_type = 'BFC'
sess = tf.InteractiveSession(config=config)

training_data, training_label = read()
test_data, test_label = read(dataset="testing")

with tf.device('/cpu:0'):
 # One hot encoding
  training_label = sess.run(tf.one_hot(indices=training_label, depth=max(training_label + 1), dtype=np.float64))
  test_label = sess.run(tf.one_hot(indices=test_label, depth=max(test_label + 1), dtype=np.float64))

cnn = CNN(epochs=2,batch_size=800,isSave=False)
# output layer
y_ = cnn.initial_mlp_network()
cnn_accuricy, cnn_predict_label = cnn.calculate_session(y_, training_data, training_label, test_data, test_label)
print(cnn_accuricy,cnn_predict_label)