使用一层神经网络训练mnist数据集

import numpy as np
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
def add_layer(inputs,in_size,out_size,activation_function=None):
    W=tf.Variable(tf.random_normal([in_size,out_size]))
    b=tf.Variable(tf.zeros([1,out_size])+0.01)
    Z=tf.matmul(inputs,W)+b
    if activation_function is None:
        out_puts=Z
    else:
        out_puts=activation_function(Z)
    return out_puts
if __name__=="__main__":
    MINST=input_data.read_data_sets("./",one_hot=True)
    learning_rate=0.05
    batch_size=128
    n_epochs=10
    X=tf.placeholder(tf.float32,[batch_size,784])
    Y=tf.placeholder(tf.float32,[batch_size,10])
    L1=add_layer(X,784,1000,tf.nn.relu)
    prediction=add_layer(L1,1000,10)
    entropy=tf.nn.softmax_cross_entropy_with_logits(labels=Y,logits=prediction)
    loss=tf.reduce_mean(entropy)
    optimizer=tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)
    init=tf.global_variables_initializer()
    with tf.Session() as sess:
        sess.run(init)
        n_batches=int(MINST.train.num_examples/batch_size)
        for i in range(n_epochs):
            for j in range(n_batches):
                X_batch,Y_batch=MINST.train.next_batch(batch_size=batch_size)
                _,loss_=sess.run([optimizer,loss],feed_dict={
                    X:X_batch,
                    Y:Y_batch
                })
                if j == 0:
                    print("Loss of epochs[{0}] batch[{1}]: {2}".format(i, j, loss_))

        # test the model
        n_batches = int(MINST.test.num_examples / batch_size)
        total_correct_preds = 0
        for i in range(n_batches):
            X_batch, Y_batch = MINST.test.next_batch(batch_size)
            preds = sess.run(prediction, feed_dict={X: X_batch, Y: Y_batch})
            correct_preds = tf.equal(tf.argmax(preds, 1), tf.argmax(Y_batch, 1))
            accuracy = tf.reduce_sum(tf.cast(correct_preds, tf.float32))

            total_correct_preds += sess.run(accuracy)

        print("Accuracy {0}".format(total_correct_preds / MINST.test.num_examples))

我们不做卷积。直接将x输入到网络中去。最后用softmax作为激活函数

大概结构，我这里没法上传，等我回去在传。