Autoencoder_semi_Vanilla.py

import tensorflow as tf
import numpy as np
import Data_handler
import matplotlib.pyplot as plt


n_input = n_output = 218
n_hidden = 210
n_hidden2 = 200
n_target_layer = 200

learning_rate = 0.01

X = tf.placeholder(dtype=tf.float32, shape=[None, n_input])

l2_reg = tf.contrib.layers.l2_regularizer(0.001)
he_init = tf.contrib.layers.variance_scaling_initializer()
def dense_layer(inputs, output, act = None, kernel_init = he_init, kernel_reg = l2_reg):
    return tf.layers.dense(inputs, output, activation=act, kernel_initializer=kernel_init, kernel_regularizer=kernel_reg)

layer_in = dense_layer(X, n_hidden)
#hidden_1 = dense_layer(layer_in, n_hidden)
target_layer = dense_layer(layer_in, n_target_layer)
#hidden_2 = dense_layer(target_layer, n_hidden)
layer_out = dense_layer(target_layer, n_output)

loss_op= tf.reduce_mean(tf.square(layer_out - X))
optimizer = tf.train.AdamOptimizer(learning_rate)
train_op = optimizer.minimize(loss_op)


#x_train = make_data(n_input, batch_size)
#x_test = make_data(n_input, batch_size)
all_data = Data_handler.generate_sparse_vectors()
batch_size= 400
data_train = all_data[:501]
print(len(data_train))
data_test = all_data[501:]
x_test = Data_handler.get_batch(data_test, 100)
epochs = 800
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    losses  = []
    eval_losses  = []
    for epoch in range(epochs):
        x_train = Data_handler.get_batch(data_train, batch_size)
        _, loss= sess.run([train_op, loss_op], feed_dict={X:x_train})
        #print(x_train[1])
        losses.append(loss)
        eval_loss = sess.run(loss_op, feed_dict={X:x_test})
        eval_losses.append(eval_loss)
        print(eval_loss, 'Epoch: ', epoch)
    results = layer_out.eval(feed_dict={X:x_test})
        #if epoch % 100 == 0:
        #    x_train = results
    #low_dim_rep = target_layer.eval(feed_dict={X:x_test})


for i in [1,4,6,8,15]:
    #print(x_test[i])
    results = [[int(x) for x in l] for l in results]
    j = 0
    for j in range(len(x_test[i])):
        if x_test[i][j] != 0:
            print(x_test[i][j], results[i][j])
    print(x_test[i])
    print(results[i])
        
plt.plot(losses, 'r', label='train')
plt.plot(eval_losses, 'g', label='eval')
plt.legend(loc='upper right')
plt.show()

#print(low_dim_rep)