学习进度笔记18

观看Tensorflow案例实战视频课程18 训练RNN网络

def _RNN(_X,_W,_b,_nsteps,_name):
    #1.Permute input from [batchsize,nsteps,diminput]
    #  =>[nsteps,batchsize,diminput]
    _X=tf.transpose(_X,[1,0,2])
    #2.Reshape input to [nsteps*batchsize,diminput]
    _X=tf.reshape(_X,[-1,diminput])
    #3.Input layer => Hidden layer
    _H=tf.matmul(_X,_W['hidden'])+_b['hidden']
    #4.Splite data to 'nsteps' chunks. An i_th chunck indicates i_th batch data
    _Hsplit=tf.split(0,_nsteps,_H)
    #5.Get LSTM's final output (_LSTM_O) and state (_LSTM_S)
    #  Both _LSTM_O and _LSTM_S consist of 'batchsize' elements
    #  Only _LSTM_O will be used to Predict the output.
    with tf.variable_scope(_name) as scope:
        scope.reuse_variables()
        lstm_cell=tf.nn.run_cell.BasicLSTMCell(dimhidden,forget_bias=1.0)
        _LSTM_O,_LSTM_S=tf.nn.rnn(lstm_cell,_Hsplit,dtype=tf.float32)
    #6.Output
    _O=tf.matmul(_LSTM_O[-1],_W['out'])+_b['out']
    #Return!
    return{
        'X':_X,'H':_H,'Hsplit':_Hsplit,
        'LSTM_O':_LSTM_O,'LSTM_S':_LSTM_S,'O':_O
    }
print("Network ready")

learning_rate=0.001
x=tf.placeholder("float",[None,nsteps,diminput])
y=tf.placeholder("float",[None,dimoutput])
myrnn=_RNN(x,weights,biases,nsteps,'basic')
#myrnn=_RNN(x,weights,biases,nsteps,'basic1')
pred=myrnn['O']
cost=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred,y))
optm=tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)#Adam Optimizer
accr=tf.reduce_mean(tf.cast(tf.equal(tf.argmax(pred,1),tf.argmax(y,1)),tf.float32))
init=tf.global_variables_initializer()
print("Network Ready!")

training_epochs=5
batch_size=16
display_step=1
sess=tf.Session()
sess.run(init)
for epoch in range(training_epochs):
    avg_cost=0
    #total_batch=int(mnist.train.num_examples/batch_size)
    total_batch=100
    #Loop over all batches
    for i in range(total_batch):
       batch_xs,batch_ys=mnist.train.next_batch(batch_size)
       batch_xs=batch_xs.reshape((batch_size,nsteps,diminput))
       #Fit training using batch data
       feeds={x:batch_xs,y:batch_ys}
       sess.run(optm,feed_dict=feeds)
       #Compute average loss
       avg_cost+=sess.run(cost,feed_dict=feeds)/total_batch
    #Display logs per epoch step
    if epoch % display_step==0:
        print("Epoch:%03d/%03d cost:%.9f" % (epoch,training_epochs,avg_cost))
        feeds={x:batch_xs,y:batch_ys}
        train_acc=sess.run(accr,feed_dict=feeds)
        print("Training accuracy:%.3f" % (train_acc))
        testimgs=testimgs.reshape((ntest,nsteps,diminput))
        feeds={x:testimgs,y:testlabels,istate:np.zeros((intest,2*dimhidden))}
        test_acc=sess.run(accr,feed_dict=feeds)
        print("Test accuracy:%.3f" % (test_acc))
print("Optimization Finished.")