tensor.py

import numpy as np
import tensorflow as tf

import matplotlib as mpl
mpl.use('TkAgg')
import matplotlib.pyplot as plt

from util import get_normalized_data, cost, error_rate, y2indicator


def main():
    X, Y = get_normalized_data()

    max_iter = 30
    print_period = 10
    lr = 0.00003
    reg = 0.01
    Xtrain = X[:-1000]
    Ytrain = Y[:-1000]
    Xtest = X[-1000:]
    Ytest = Y[-1000:]
    Ytrain_ind = y2indicator(Ytrain)
    Ytest_ind = y2indicator(Ytest)

    N, D = Xtrain.shape
    batch_sz = 512
    n_batches = int(N / batch_sz)

    M1 = 300
    M2 = 100
    K = 10
    W1_init = np.random.randn(D, M1) / (2*np.sqrt(D+M1))
    b1_init = np.zeros((M1))
    W2_init = np.random.rand(M1,M2) / (2*np.sqrt(M1+M2))
    b2_init = np.zeros((M2))
    W3_init = np.random.rand(M2,K) / (2*np.sqrt(M2+K))
    b3_init = np.zeros((K))

    X = tf.placeholder(tf.float32, shape=(None, D), name ='X')
    T = tf.placeholder(tf.float32, shape=(None, K), name = 'T')
    W1 = tf.Variable(W1_init.astype(np.float32))
    b1 = tf.Variable(b1_init.astype(np.float32))
    W2 = tf.Variable(W2_init.astype(np.float32))
    b2 = tf.Variable(b2_init.astype(np.float32))
    W3 = tf.Variable(W3_init.astype(np.float32))
    b3 = tf.Variable(b3_init.astype(np.float32))

    Z1 = tf.nn.relu( tf.matmul(X, W1) + b1)
    Z2 = tf.nn.relu( tf.matmul(Z1, W2) + b2)
    Yish = tf.matmul(Z2, W3) + b3

    cost = tf.reduce_sum(tf.nn.softmax_cross_entropy_with_logits(logits=Yish, labels = T))

    train_op = tf.train.RMSPropOptimizer(lr, decay = 0.99, momentum=0.9).minimize(cost)

    predict_op = tf.argmax(Yish, 1)

    LL = []
    init = tf.initialize_all_variables()
    with tf.Session() as sess:
        sess.run(init)

        for i in range(max_iter):
            for j in range(n_batches):
                Xbatch = Xtrain[j*batch_sz:(j*batch_sz + batch_sz)]
                Ybatch = Ytrain_ind[j*batch_sz:(j*batch_sz + batch_sz)]
                                     
                sess.run(train_op, feed_dict={X: Xbatch, T: Ybatch})
                if j % print_period == 0:
                    test_cost = sess.run(cost, feed_dict={X: Xtest, T: Ytest_ind})
                    prediction = sess.run(predict_op, feed_dict={X: Xtest})
                    err = error_rate(prediction, Ytest)
                    print("Cost / err at iteration i=%d, j=%d: %.3f / %.3f" % (i, j, test_cost, err))
                LL.append(test_cost)
    plt.plot(LL)
    plt.show

if __name__ == '__main__':
    main()