TensorMLP2.py 文件源码

def tensorFlowBasic(X_train, y_train, X_val, y_val, X_test, y_test):
    sess = tf.InteractiveSession()
    x = tf.placeholder("float", shape=[None, 400])
    y_ = tf.placeholder("float", shape=[None, 10])
    W = tf.Variable(tf.zeros([400, 10]))
    b = tf.Variable(tf.zeros([10]))
    sess.run(tf.initialize_all_variables())
    y = tf.nn.softmax(tf.matmul(x, W) + b)
    cross_entropy = -tf.reduce_sum(y_ * tf.log(y))
    train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
    mydata = read_data_sets(X_train, y_train, X_val, y_val, X_test, y_test)

    for i in range(1000):
        batch = mydata.train.next_batch(50)
        train_step.run(feed_dict={x: batch[0], y_: batch[1]})

    correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
    return accuracy.eval(feed_dict={x: mydata.test.images, y_: mydata.test.labels})