TF之LSTM：利用LSTM算法对mnist手写数字图片数据集(TF函数自带)训练、评估(偶尔100%准确度，交叉熵验证)
目录
输出结果
设计思路
代码设计
输出结果
第 0 accuracy 0.125第 20 accuracy 0.6484375第 40 accuracy 0.78125第 60 accuracy 0.9296875第 80 accuracy 0.8671875第 100 accuracy 0.90625第 120 accuracy 0.8671875第 140 accuracy 0.8671875第 160 accuracy 0.8671875第 180 accuracy 0.921875第 200 accuracy 0.890625第 220 accuracy 0.953125第 240 accuracy 0.921875第 260 accuracy 0.9296875第 280 accuracy 0.9140625第 300 accuracy 0.921875第 320 accuracy 0.9609375第 340 accuracy 0.953125第 360 accuracy 0.984375第 380 accuracy 0.921875第 400 accuracy 0.9453125第 420 accuracy 0.921875第 440 accuracy 0.9296875第 460 accuracy 0.96875第 480 accuracy 0.984375第 500 accuracy 0.96875第 520 accuracy 0.953125第 540 accuracy 0.96875第 560 accuracy 0.953125第 580 accuracy 0.9921875第 600 accuracy 0.984375第 620 accuracy 0.953125第 640 accuracy 0.953125第 660 accuracy 0.9921875第 680 accuracy 0.96875第 700 accuracy 0.9765625第 720 accuracy 0.96875第 740 accuracy 0.9921875第 760 accuracy 0.984375第 780 accuracy 0.953125
设计思路
代码设计
import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets('MNIST_data', one_hot=True)lr=0.001 training_iters=100000batch_size=128n_inputs=28 n_steps=28 n_hidden_units=128 n_classes=10 x=tf.placeholder(tf.float32, [None,n_steps,n_inputs])y=tf.placeholder(tf.float32, [None,n_classes])weights ={'in':tf.Variable(tf.random_normal([n_inputs,n_hidden_units])),'out':tf.Variable(tf.random_normal([n_hidden_units,n_classes])),}biases ={'in':tf.Variable(tf.constant(0.1,shape=[n_hidden_units,])),'out':tf.Variable(tf.constant(0.1,shape=[n_classes,])),}def RNN(X,weights,biases): X=tf.reshape(X,[-1,n_inputs])X_in=tf.matmul(X,weights['in'])+biases['in'] X_in=tf.reshape(X_in,[-1,n_steps,n_hidden_units])lstm_cell=tf.nn.rnn_cell.BasicLSTMCell(n_hidden_units,forget_bias=1.0,state_is_tuple=True)__init__state=lstm_cell.zero_state(batch_size, dtype=tf.float32)outputs,states=tf.nn.dynamic_rnn(lstm_cell,X_in,initial_state=__init__state,time_major=False)outputs=tf.unpack(tf.transpose(outputs, [1,0,2]))results=tf.matmul(outputs[-1],weights['out'])+biases['out']return resultspred =RNN(x,weights,biases)cost =tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y)) train_op=tf.train.AdamOptimizer(lr).minimize(cost)correct_pred=tf.equal(tf.argmax(pred,1),tf.argmax(y,1))accuracy=tf.reduce_mean(tf.cast(correct_pred,tf.float32)) <br>with tf.Session() as sess: sess.run(init)step=0while step*batch_size < training_iters:batch_xs,batch_ys=mnist.train.next_batch(batch_size)batch_xs=batch_xs.reshape([batch_size,n_steps,n_inputs])sess.run([train_op],feed_dict={x:batch_xs,y:batch_ys,})if step%20==0:print(sess.run(accuracy,feed_dict={x:batch_xs,y:batch_ys,}))step+=1
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TF之LSTM：利用LSTM算法对mnist手写数字图片数据集训练、评估(偶尔100%准确度)
TF之LSTM：利用LSTM算法对mnist手写数字图片数据集(TF函数自带)训练 评估(偶尔100%准确度 交叉熵验证)
