def createLayer(index, numOfOutput):
training_data = np.array(Datasplit[index], dtype=np.float32)
training_label = np.array(DatasplitLabel[index], dtype=np.float32)
test_data = np.array(Datatest[index], dtype=np.float32)
test_label = np.array(DatatestLabel[index], dtype=np.float32)
autoencoder = AutoEncoder(numOfOutput=numOfOutput, epochs=10)
decoder_op, layer_1 = autoencoder.initial_autoencode_network('encoder_h1', 'encoder_b1', 'decoder_h1', 'decoder_b1',
autoencoder._X)
autoencoder.calculate_AutoEncoder(decoder_op, training_data, training_label, autoencoder._X)
decoder_op, layer_2 = autoencoder.initial_autoencode_network('encoder_h2', 'encoder_b2', 'decoder_h2', 'decoder_b2',
layer_1)
autoencoder.calculate_AutoEncoder(decoder_op, training_data, training_label, layer_1)
decoder_op, layer_3 = autoencoder.initial_autoencode_network('encoder_h3', 'encoder_b3', 'decoder_h3', 'decoder_b3',
layer_2)
autoencoder.calculate_AutoEncoder(decoder_op, training_data, training_label, layer_2)
y_ = autoencoder.initial_mlp_network()
with tf.device('/cpu:0'):
training_label_onehot = sess.run(
tf.one_hot(indices=training_label, depth=numOfOutput, dtype=np.float64))
test_label_onehot = sess.run(
tf.one_hot(indices=test_label, depth=numOfOutput, dtype=np.float64))
cnn_accuricy, cnn_predict_label = autoencoder.calculate_session(y_, training_data, training_label_onehot, test_data,
test_label_onehot)
print(cnn_accuricy)
return cnn_accuricy
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))
autoencoder = AutoEncoder()
decoder_op, layer_1 = autoencoder.initial_autoencode_network(
'encoder_h1', 'encoder_b1', 'decoder_h1', 'decoder_b1', autoencoder._X)
autoencoder.calculate_AutoEncoder(decoder_op, training_data, training_label,
autoencoder._X)
decoder_op, layer_2 = autoencoder.initial_autoencode_network(
'encoder_h2', 'encoder_b2', 'decoder_h2', 'decoder_b2', layer_1)
autoencoder.calculate_AutoEncoder(decoder_op, training_data, training_label,
layer_1)
decoder_op, layer_3 = autoencoder.initial_autoencode_network(
'encoder_h3', 'encoder_b3', 'decoder_h3', 'decoder_b3', layer_2)
autoencoder.calculate_AutoEncoder(decoder_op, training_data, training_label,
layer_2)
y_ = autoencoder.initial_mlp_network()
cnn_accuricy, cnn_predict_label = autoencoder.calculate_session(
y_, training_data, training_label, test_data, test_label)
print(cnn_accuricy)
