学习进度笔记12

观看Tensorflow案例实战视频课程12 卷积神经网络模型参数

def conv_basic(_input,_w,_b,_keepratio):
    #INPUT
    _input_r=tf.reshape(_input,shape=[-1,28,28,1])
    #CONV LAYER 1
    _conv1=tf.nn.conv2d(_input_r,_w['wc1'],strides=[1,1,1,1],padding='SAME')
    #_mean,_var=tf.nn.moments(_conv1,[0,1,2])
    #_conv1=tf.nn.batch_normalization(_conv1,_mean,_var,0,1,0.0001)
    _conv1=tf.nn.relu(tf.nn.bias_add(_conv1,_b['bc1']))
    _pool1=tf.nn.max_pool(_conv1,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')
    _pool_dr1=tf.nn.dropout(_pool1,_keepratio)
    #CONV LEYER 2
    _conv2=tf.nn.conv2d(_pool_dr1,_w['wc2'],strides=[1,1,1,1],padding='SAME')
    #_mean,_var=tf.nn.moments(_conv2,[0,1,2])
    #_conv2=tf.nn.batch_normalization(_conv2,_mean,_var,0,1,0.0001)
    _conv2=tf.nn.relu(tf.nn.bias_add(_conv2,_b['bc2']))
    _pool2=tf.nn.max_pool(_conv2,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')
    _pool_dr2=tf.nn.dropout(_pool2,_keepratio)
    #VECTORIZE
    _densel=tf.reshape(_pool_dr2,[-1,_w['wd1'].get_shape().as_list()[0]])
    #FULLY CONNECTED LAVER 1
    _fc1=tf.nn.relu(tf.add(tf.matmul(_densel,_w['wd1']),_b['bd1']))
    _fc_dr1=tf.nn.dropout(_fc1,_keepratio)
    #FULLY CONNECTED LAVER 2
    _out=tf.add(tf.matmul(_fc_dr1,_w['wd2']),_b['bd2'])
    #RETURN
    out={'input_r':_input_r,'conv1':_conv1,'pool1':_pool1,'pool1_dr1':_pool_dr1,
        'conv2':_conv2,'pool2':_pool2,'pool_dr2':_pool_dr2,'densel':_densel,
        'fc1':_fc1,'fc_dr1':_fc_dr1,'out':_out 
    }
    return out
print("CNN READY")

a=tf.Variable(tf.random_normal([3,3,1,64],stddev=0.1))
print(a)
a=tf.Print(a,[a],"a；")
init=tf.global_variables_initializer()
sess=tf.Session()
sess.run(init)
#sess.run(a)

print(help(tf.nn.conv2d))
print(help(tf.nn.max_pool))

x=tf.placeholder(tf.float32,[None,n_input])
y=tf.placeholder(tf.float32,[None,n_output])
keepratio=tf.placeholder(tf.float32)

#FUNCTIONS

_pred=conv_basic(x,weights,biases,keepratio)['out']
cost=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(_pred,y))
optm=tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)
_corr=tf.equal(tf.argmax(_pred,1),tf.argmax(y,1))
accr=tf.reduce_mean(tf.cast(_corr,tf.float32))
init=tf.global_variables_initializer()

#SAVER
print("GRAPH READY")

sess=tf.Session()
sess.run(init)

training_epochs=15
batch_size=16
display_step=1
for epoch in range(training_epochs):
    avg_cost=0
    #total_batch=int(mnist.train.num_examples/batch_size)
    total_batch=10
    #Loop over all batches
    for i in range(total_batch):
       batch_xs,batch_ys=mnist.train.next_batch(batch_size)
       #Fit training using batch data
       sess.run(optm,feed_dict={x:batch_xs,y:batch_ys,keepratio:0.7})
       #Compute average loss
       avg_cost+=sess.run(cost,feed_dict={x:batch_xs,y:batch_ys,keepratio:1.})/total_batch
    
    #Display logs per epoch step
    if epoch % display_step==0:
        print("Epoch:%03d/%03d cost:%.9f" % (epoch,training_epochs,avg_cost))
        train_acc=sess.run(accr,feed_dict={x:batch_xs,y:batch_ys,keepratio:1.})
        print("Training accuracy:%.3f" % (train_acc))
        #test_acc=sess.run(accr,feed_dict={x:testimg,y:testlabel,keepratio:1.})
        #print("Test accuracy:%.3f" % (test_acc))
        
print("OPTIMIZATION FINISHED")