Tensorflow 实战 笔记

TensorFlow第一步from tensorflow.examples.tutorials.mnist import input_datamnist=input_data.read_data_sets("MNIST_DATA/",one_hot=True)print(mnist.train.images.shape,mnist.train.labels.shape)print (mnist.test.images.shape, mnist.test.labels.shape)print (mnist.validation.images.shape, mnist.validation.labels.shape)import tensorflow as tfsess=tf.InteractiveSession()x=tf.placeholder(tf.float32,[None,784])W=tf.Variable(tf.zeros([784,10]))b=tf.Variable(tf.zeros([10]))y=tf.nn.softmax(tf.matmul(x,W)+b)y_=tf.placeholder(tf.float32,[None,10])cross_entropy=tf.reduce_mean(-tf.reduce_sum(y_* tf.log(y),reduction_indices=[1]))train_step=tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)tf.global_variables_initializer().run()for i in range(1000):    batch_xs, batch_ys=mnist.train.next_batch(100)    train_step.run({x:batch_xs,y_:batch_ys})correct_prediction=tf.equal(tf.argmax(y,1),tf.argmax(y_,1))accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))print(accuracy.eval({x:mnist.test.images,y_:mnist.test.labels}))自编码器import numpy as npimport sklearn.preprocessing as prepimport tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_datadef xavier_int(fan_in,fan_out,constant=1):    low=-constant*np.sqrt(6.0/(fan_in+fan_out))    high=constant*np.sqrt(6.0/(fan_in+fan_out))    return tf.random_uniform((fan_in,fan_out), minval=low, maxval=high,dtype=tf.float32)class AdditiveGaussianNoiseAutoencoder(object):    def __init__(self,n_input,n_hidden,transfer_function=tf.nn.softplus,optimizer=tf.train.AdamOptimizer(),scale=0.1):        self.n_input=n_input        self.n_hidden=n_hidden        self.transfer=transfer_function        self.scale=tf.placeholder(tf.float32)        self.training_scale=scale        network_weights=self._initialize_weights()        self.weights=network_weights        self.x=tf.placeholder(tf.float32,[None,self.n_input])        self.hidden=self.transfer(tf.add(tf.matmul(self.x+scale*tf.random_normal((n_input,)),self.weights['w1']),self.weights['b1']))        self.reconstruction=tf.add(tf.matmul(self.hidden,self.weights['w2']),self.weights['b2'])        self.cost=0.5*tf.reduce_sum(tf.pow(tf.subtract(self.reconstruction,self.x),2.0))        self.optimizer=optimizer.minimize(self.cost)        init=tf.global_variables_initializer()        self.sess=tf.Session()        self.sess.run(init)    def _initialize_weights(self):        all_weights=dict()        all_weights['w1']=tf.Variable(xavier_int(self.n_input,self.n_hidden))        all_weights['b1']=tf.Variable(tf.zeros([self.n_hidden],dtype=tf.float32))        all_weights['w2']=tf.Variable(tf.zeros([self.n_hidden,self.n_input],dtype=tf.float32))        all_weights['b2']=tf.Variable(tf.zeros([self.n_input],dtype=tf.float32))        return all_weights    def partial_fit(self,X):        cost,opt=self.sess.run((self.cost,self.optimizer),feed_dict={self.x:X,self.scale:self.training_scale})        return cost    def calc_total_cost(self,X):        return self.sess.run(self.cost,feed_dict={self.x:X,self.scale:self.training_scale})    def transform(self,X):        return self.sess.run(self.hidden, feed_dict={self.x:X,self.scale:self.training_scale})    def generate(self,hidden=None):        if hidden is None:            hidden=np.random.normal(size=self.weights['b1'])        return self.sess.run(self.reconstruction,feed_dict={self.hidden:hidden})    def reconstruct(self,X):        return self.sess.run(self.reconstruct,feed_dict={self.x:X,self.scale:self.training_scale})    def getWeights(self):        return self.sess.run(self.weights['w1'])    def getBiases(self):        return self.sess.run(self.weights['b1'])mnist=input_data.read_data_sets('MNIST_data',one_hot=True)def standard_scale(X_train,X_test):    preprocessor=prep.StandardScaler().fit(X_train)    X_train=preprocessor.transform(X_train)    X_test=preprocessor.transform(X_test)    return X_train, X_testX_train, X_test=standard_scale(mnist.train.images,mnist.train.images)n_samples=int(mnist.train.num_examples)training_epochs=20batch_size=128display_step=1autoencoder=AdditiveGaussianNoiseAutoencoder(n_input=784,n_hidden=200,transfer_function=tf.nn.softplus,optimizer=tf.train.AdamOptimizer(learning_rate=0.001),scale=0.01)for epoch in range(training_epochs):    avg_cost=0.    total_batch=int(n_samples/batch_size)    for i in range(total_batch):        batch_xs = get_random_block_from_data(X_train, batch_size)        cost=autoencoder.partial_fit(batch_xs)        avg_cost+=cost/n_samples*batch_size    if epoch % display_step==0:        print("Epoch:",'%04d' % (epoch+1),"cost=","{:.9f}".format(avg_cost))        print("Total cost:"+str(autoencoder.calc_total_cost(X_test)))实现多层感知机：from tensorflow.examples.tutorials.mnist import input_dataimport tensorflow as tfmnist=input_data.read_data_sets("MNIST_data/",one_hot=True)sess=tf.InteractiveSession()in_units=784h1_units=300W1=tf.Variable(tf.truncated_normal([in_units,h1_units],stddev=0.1))b1=tf.Variable(tf.zeros(h1_units))W2=tf.Variable(tf.zeros([h1_units,10]))b2=tf.Variable(tf.zeros([10]))x=tf.placeholder(tf.float32,[None,in_units])keep_prob=tf.placeholder(tf.float32)hidden1=tf.nn.relu(tf.matmul(x,W1)+b1)hidden1_drop=tf.nn.dropout(hidden1,keep_prob)y=tf.nn.softmax(tf.matmul(hidden1_drop,W2)+b2)y_=tf.placeholder(tf.float32,[None,10])cross_entropy=tf.reduce_mean(-tf.reduce_sum(y_*tf.log(y),reduction_indices=[1]))train_step=tf.train.AdagradOptimizer(0.3).minimize(cross_entropy)tf.global_variables_initializer().run()for i in range(3000):    batch_xs,batch_ys=mnist.train.next_batch(100)    train_step.run({x:batch_xs,y_:batch_ys,keep_prob:0.75})correct_prediction=tf.equal(tf.argmax(y,1),tf.argmax(y_,1))accuary=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))print(accuary.eval({x:mnist.test.images,y_:mnist.test.labels,keep_prob:1.0}))简单版本的卷积网络from tensorflow.examples.tutorials.mnist import input_dataimport tensorflow as tfmnist=input_data.read_data_sets("MNIST_data/",one_hot=True)sess=tf.InteractiveSession()def weight_variable(shape):    initial=tf.truncated_normal(shape,stddev=0.1)    return tf.Variable(initial)def bias_variable(shape):    initial=tf.constant(0.1,shape=shape)    return tf.Variable(initial)def conv2d(x,W):    return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME')def max_pool_2x2(x):    return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')x=tf.placeholder(tf.float32,[None,784])y_=tf.placeholder(tf.float32,[None,10])x_image=tf.reshape(x,[-1,28,28,1])W_conv1=weight_variable([5,5,1,32])b_conv1=bias_variable([32])h_conv1=tf.nn.relu(conv2d(x_image,W_conv1)+b_conv1)h_pool1=max_pool_2x2(h_conv1)W_conv2=weight_variable([5,5,32,64])b_conv2=bias_variable([64])h_conv2=tf.nn.relu(conv2d(h_pool1,W_conv2)+b_conv2)h_pool2=max_pool_2x2(h_conv2)W_fc1=weight_variable([7*7*64,1024])b_fc1=bias_variable([1024])h_pool2_flat=tf.reshape(h_pool2,[-1,7*7*64])h_fc1=tf.nn.relu(tf.matmul(h_pool2_flat,W_fc1)+b_fc1)keep_prob=tf.placeholder(tf.float32)h_fc1_drop=tf.nn.dropout(h_fc1,keep_prob)W_fc2=weight_variable([1024,10])b_fc2=bias_variable([10])y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2)+b_fc2)cross_entropy=tf.reduce_mean(-tf.reduce_sum(y_*tf.log(y_conv),reduction_indices=[1]))train_step=tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)correct_prediction=tf.equal(tf.argmax(y_conv,1),tf.argmax(y_,1))accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))tf.global_variables_initializer().run()for i in range(20000):    batch=mnist.train.next_batch(50)    if i%100==0:        train_accuary=accuracy.eval(feed_dict={ x:batch[0],y_:batch[1],keep_prob:1.0})        print("step %d trainning accuracy %g" % (i,train_accuary))    train_step.run(feed_dict={x:batch[0],y_:batch[1],keep_prob:0.5})print ("test accuracy %g"%accuracy.eval(feed_dict={x:mnist.test.images,y_:mnist.test.labels,keep_prob:1.0}))分布式Estimatorimport tensorflow as tffrom tensorflow.contrib import layersfrom tensorflow.contrib import learndef my_model(features,tareget):    target=tf.one_hot(tareget, 3,1,0)    features=layers.stack(features,layers.fully_connected,[10,20,10])    prediction,loss=tf.contrib.learn.models.logistic_regression_zero_init(features,target)    train_op=tf.contrib.layers.optimize_loss(loss,tf.contrib.framework.get_global_step(),optimizer='Adagrad',learning_rate=0.1)    return {'class':tf.argmax(prediction,1), 'prob':prediction}, loss, train_opfrom sklearn import datasets, cross_validationiris=datasets.loadrirs()x_train, x_test,y_train, y_test=cross_validation.train_test_split(iris.data,iris.target,test_size=0.2,random_state=32)classifiter=learn.Estimator(mode_fn=my_model)classifiter.fit(x_train,y_train,steps=700)predictions=classifiter.predict(x_test)