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  • tensorflow 代码流程02

    阶段1:变量的使用

    """
       __author__="dazhi"
        2021/3/6-12:52
    """
    import tensorflow as tf
    import os
    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
    
    def variable_demo():
        tf.compat.v1.disable_eager_execution()
        #ensure sess.run () can operate normally
        """tensorflow Basic structure"""
    
        #Demonstration of variable
    
        #Creating variables
        a1 = tf.Variable(initial_value=50)
        b1 = tf.Variable(initial_value=30)
        c1 = tf.add(a1,b1)
    
        #initialize variable
        init = tf.compat.v1.global_variables_initializer()
    
        #Return call must be turned on to display results
        with tf.compat.v1.Session() as sess:
            sess.run(init)
            #run placeholder
            a2,b2,c2 = sess.run([a1,b1,c1])
            print("The value  ",a2,"  ",b2," ",c2)
    
        return None
    
    if __name__=="__main__":
        variable_demo()

    阶段2:修改变量的命名空间

    """
       __author__="dazhi"
        2021/3/6-12:52
    """
    import tensorflow as tf
    import os
    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
    
    def variable_demo():
        tf.compat.v1.disable_eager_execution()
        #ensure sess.run () can operate normally
        """tensorflow Basic structure"""
    
        #Demonstration of variable
    
        #Creating variables
        with tf.compat.v1.variable_scope("wen"):
            a1 = tf.Variable(initial_value=50)
            b1 = tf.Variable(initial_value=30)
            c1 = tf.add(a1,b1)
            print(a1)
            print(b1)
            print(c1)
    
        #initialize variable
        init = tf.compat.v1.global_variables_initializer()
    
        #Return call must be turned on to display results
        with tf.compat.v1.Session() as sess:
            sess.run(init)
            #run placeholder
            a2,b2,c2 = sess.run([a1,b1,c1])
            print("The value  ",a2,"  ",b2," ",c2)
    
        return None
    
    if __name__=="__main__":
        variable_demo()

    阶段3:自实现线性回归

    """
       __author__="dazhi"
        2021/3/6-13:35
    """
    import tensorflow as tf
    import os
    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
    
    #Self realization of a linear regression
    def linear_regression():
        tf.compat.v1.disable_eager_execution()
        #ensure sess.run () can operate normally
        """tensorflow Basic structure"""
    
        #1 Prepare data
    
        x = tf.compat.v1.random_normal(shape=[100,1])
        y_true = tf.matmul(x,[[0.8]])+0.7
    
        #2 construct model
    
        #Defining model parameters using variables
        weights = tf.Variable(initial_value=tf.compat.v1.random_normal(shape=[1,1]))
        bias = tf.Variable(initial_value=tf.compat.v1.random_normal(shape=[1,1]))
        y_predict = tf.matmul(x,weights)+bias
    
        #3 construct loss function
    
        error = tf.reduce_mean(tf.square(y_predict - y_true))
    
        #4 optimize loss function
    
        optimizer = tf.compat.v1.train.GradientDescentOptimizer(learning_rate=0.01).minimize(error)
    
    
        #initialize variable
        init = tf.compat.v1.global_variables_initializer()
    
        #Return call must be turned on to display results
        with tf.compat.v1.Session() as sess:
            sess.run(init)
            #Query the value of initialization operation model parameters
            print("Before training, the model parameters were as follows:weight %f ,bias %f , loss %f " % (weights.eval(),bias.eval(),error.eval()))
    
            #Start training
            for i in range(1000):
                sess.run(optimizer)
            print("After training, the model parameters were as follows:weight %f ,bias %f, loss %f " % (weights.eval(),bias.eval(),error.eval()))
    
        return None
    
    if __name__=="__main__":
        linear_regression()

    阶段4:可视化展示线性回归中的数据

    """
       __author__="dazhi"
        2021/3/6-13:35
    """
    import tensorflow as tf
    import os
    
    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
    
    
    # Self realization of a linear regression
    def linear_regression():
        tf.compat.v1.disable_eager_execution()
        # ensure sess.run () can operate normally
        """tensorflow Basic structure"""
    
        # 1 Prepare data
    
        x = tf.compat.v1.random_normal(shape=[100, 1])
        y_true = tf.matmul(x, [[0.8]]) + 0.7
    
        # 2 construct model
    
        # Defining model parameters using variables
        weights = tf.Variable(initial_value=tf.compat.v1.random_normal(shape=[1, 1]))
        bias = tf.Variable(initial_value=tf.compat.v1.random_normal(shape=[1, 1]))
        y_predict = tf.matmul(x, weights) + bias
    
        # 3 construct loss function
    
        error = tf.reduce_mean(tf.square(y_predict - y_true))
    
        # 4 optimize loss function
    
        optimizer = tf.compat.v1.train.GradientDescentOptimizer(learning_rate=0.01).minimize(error)
    
        # 2++Collect variables
        tf.compat.v1.summary.scalar("error", error)
        tf.compat.v1.summary.histogram("weights", weights)
        tf.compat.v1.summary.histogram("bias", bias)
    
        # 3++Merge variables
        merged = tf.compat.v1.summary.merge_all()
    
        # initialize variable
        init = tf.compat.v1.global_variables_initializer()
    
        # Return call must be turned on to display results
        with tf.compat.v1.Session() as sess:
            sess.run(init)
    
            # 1++Create event file
            file_writer = tf.compat.v1.summary.FileWriter("./tmp/Linear", graph=sess.graph)
    
            # Query the value of initialization operation model parameters
            print("Before training, the model parameters were as follows:weight %f ,bias %f , loss %f " % (weights.eval(), bias.eval(), error.eval()))
    
            # Start training
            for i in range(1000):
                sess.run(optimizer)
    
                # Run merge variable operation
                summary = sess.run(merged)
    
                # Write the data after each iteration to the event file
                file_writer.add_summary(summary, i)
    
            print("After training, the model parameters were as follows:weight %f ,bias %f, loss %f " % (weights.eval(), bias.eval(), error.eval()))
    
        return None
    
    
    if __name__ == "__main__":
        linear_regression()

     

     

     

     

     

    阶段5:增加命名空间,使代码变得清晰

    """
       __author__="dazhi"
        2021/3/6-13:35
    """
    import tensorflow as tf
    import os
    
    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
    
    
    # Self realization of a linear regression
    def linear_regression():
        tf.compat.v1.disable_eager_execution()
        # ensure sess.run () can operate normally
        """tensorflow Basic structure"""
    
        # 1 Prepare data
        with tf.compat.v1.variable_scope("prepare_data"):
            x = tf.compat.v1.random_normal(shape=[100, 1])
            y_true = tf.matmul(x, [[0.8]]) + 0.7
    
        # 2 construct model
    
        # Defining model parameters using variables
        with tf.compat.v1.variable_scope("create_model"):
            weights = tf.Variable(initial_value=tf.compat.v1.random_normal(shape=[1, 1]))
            bias = tf.Variable(initial_value=tf.compat.v1.random_normal(shape=[1, 1]))
            y_predict = tf.matmul(x, weights) + bias
    
        # 3 construct loss function
        with tf.compat.v1.variable_scope("loss_function"):
            error = tf.reduce_mean(tf.square(y_predict - y_true))
    
        # 4 optimize loss function
        with tf.compat.v1.variable_scope("optimizer"):
            optimizer = tf.compat.v1.train.GradientDescentOptimizer(learning_rate=0.01).minimize(error)
    
        # 2++Collect variables
        tf.compat.v1.summary.scalar("error", error)
        tf.compat.v1.summary.histogram("weights", weights)
        tf.compat.v1.summary.histogram("bias", bias)
    
        # 3++Merge variables
        merged = tf.compat.v1.summary.merge_all()
    
        # initialize variable
        init = tf.compat.v1.global_variables_initializer()
    
        # Return call must be turned on to display results
        with tf.compat.v1.Session() as sess:
            sess.run(init)
    
            # 1++Create event file
            file_writer = tf.compat.v1.summary.FileWriter("./tmp/Linear", graph=sess.graph)
    
            # Query the value of initialization operation model parameters
            print("Before training, the model parameters were as follows:weight %f ,bias %f , loss %f " % (weights.eval(), bias.eval(), error.eval()))
    
            # Start training
            for i in range(1000):
                sess.run(optimizer)
    
                # Run merge variable operation
                summary = sess.run(merged)
    
                # Write the data after each iteration to the event file
                file_writer.add_summary(summary, i)
    
            print("After training, the model parameters were as follows:weight %f ,bias %f, loss %f " % (weights.eval(), bias.eval(), error.eval()))
    
        return None
    
    
    if __name__ == "__main__":
        linear_regression()

    阶段6:修改指令名称

    """
       __author__="dazhi"
        2021/3/6-13:35
    """
    import tensorflow as tf
    import os
    
    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
    
    
    # Self realization of a linear regression
    def linear_regression():
        tf.compat.v1.disable_eager_execution()
        # ensure sess.run () can operate normally
        """tensorflow Basic structure"""
    
        # 1 Prepare data
        with tf.compat.v1.variable_scope("prepare_data"):
            x = tf.compat.v1.random_normal(shape=[100, 1], name="feature")
            y_true = tf.matmul(x, [[0.8]]) + 0.7
    
        # 2 construct model
    
        # Defining model parameters using variables
        with tf.compat.v1.variable_scope("create_model"):
            weights = tf.Variable(initial_value=tf.compat.v1.random_normal(shape=[1, 1]), name="Weights")
            bias = tf.Variable(initial_value=tf.compat.v1.random_normal(shape=[1, 1]), name="Bias")
            y_predict = tf.matmul(x, weights) + bias
    
        # 3 construct loss function
        with tf.compat.v1.variable_scope("loss_function"):
            error = tf.reduce_mean(tf.square(y_predict - y_true))
    
        # 4 optimize loss function
        with tf.compat.v1.variable_scope("optimizer"):
            optimizer = tf.compat.v1.train.GradientDescentOptimizer(learning_rate=0.01).minimize(error)
    
        # 2++Collect variables
        tf.compat.v1.summary.scalar("error", error)
        tf.compat.v1.summary.histogram("weights", weights)
        tf.compat.v1.summary.histogram("bias", bias)
    
        # 3++Merge variables
        merged = tf.compat.v1.summary.merge_all()
    
        # initialize variable
        init = tf.compat.v1.global_variables_initializer()
    
        # Return call must be turned on to display results
        with tf.compat.v1.Session() as sess:
            sess.run(init)
    
            # 1++Create event file
            file_writer = tf.compat.v1.summary.FileWriter("./tmp/Linear", graph=sess.graph)
    
            # Query the value of initialization operation model parameters
            print("Before training, the model parameters were as follows:weight %f ,bias %f , loss %f " % (weights.eval(), bias.eval(), error.eval()))
    
            # Start training
            for i in range(1000):
                sess.run(optimizer)
    
                # Run merge variable operation
                summary = sess.run(merged)
    
                # Write the data after each iteration to the event file
                file_writer.add_summary(summary, i)
    
            print("After training, the model parameters were as follows:weight %f ,bias %f, loss %f " % (weights.eval(), bias.eval(), error.eval()))
    
        return None
    
    
    if __name__ == "__main__":
        linear_regression()

    阶段7:模型的保存和加载

    """
       __author__="dazhi"
        2021/3/6-13:35
    """
    import tensorflow as tf
    import os
    
    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
    
    
    # Self realization of a linear regression
    def linear_regression():
        tf.compat.v1.disable_eager_execution()
        # ensure sess.run () can operate normally
        """tensorflow Basic structure"""
    
        # 1 Prepare data
        with tf.compat.v1.variable_scope("prepare_data"):
            x = tf.compat.v1.random_normal(shape=[100, 1], name="feature")
            y_true = tf.matmul(x, [[0.8]]) + 0.7
    
        # 2 construct model
    
        # Defining model parameters using variables
        with tf.compat.v1.variable_scope("create_model"):
            weights = tf.Variable(initial_value=tf.compat.v1.random_normal(shape=[1, 1]), name="Weights")
            bias = tf.Variable(initial_value=tf.compat.v1.random_normal(shape=[1, 1]), name="Bias")
            y_predict = tf.matmul(x, weights) + bias
    
        # 3 construct loss function
        with tf.compat.v1.variable_scope("loss_function"):
            error = tf.reduce_mean(tf.square(y_predict - y_true))
    
        # 4 optimize loss function
        with tf.compat.v1.variable_scope("optimizer"):
            optimizer = tf.compat.v1.train.GradientDescentOptimizer(learning_rate=0.01).minimize(error)
    
        # 2++Collect variables
        tf.compat.v1.summary.scalar("error", error)
        tf.compat.v1.summary.histogram("weights", weights)
        tf.compat.v1.summary.histogram("bias", bias)
    
        # 3++Merge variables
        merged = tf.compat.v1.summary.merge_all()
    
        #Creating a saver object
        saver=tf.compat.v1.train.Saver()
    
        # initialize variable
        init = tf.compat.v1.global_variables_initializer()
    
        # Return call must be turned on to display results
        with tf.compat.v1.Session() as sess:
            sess.run(init)
    
            # 1++Create event file
            file_writer = tf.compat.v1.summary.FileWriter("./tmp/Linear", graph=sess.graph)
    
            # Query the value of initialization operation model parameters
            print("Before training, the model parameters were as follows:weight %f ,bias %f , loss %f " % (weights.eval(), bias.eval(), error.eval()))
    
            # Start training
            for i in range(1000):
                sess.run(optimizer)
    
                # Run merge variable operation
                summary = sess.run(merged)
    
                # Write the data after each iteration to the event file
                file_writer.add_summary(summary, i)
    
                #Save model
                if i % 10 == 0:
                    saver.save(sess, "./tmp/model/my_linear.ckpt")
    
            print("After training, the model parameters were as follows:weight %f ,bias %f, loss %f " % (weights.eval(), bias.eval(), error.eval()))
    
        return None
    
    
    if __name__ == "__main__":
        linear_regression()
    """
       __author__="dazhi"
        2021/3/6-13:35
    """
    import tensorflow as tf
    import os
    
    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
    
    
    # Self realization of a linear regression
    def linear_regression():
        tf.compat.v1.disable_eager_execution()
        # ensure sess.run () can operate normally
        """tensorflow Basic structure"""
    
        # 1 Prepare data
        with tf.compat.v1.variable_scope("prepare_data"):
            x = tf.compat.v1.random_normal(shape=[100, 1], name="feature")
            y_true = tf.matmul(x, [[0.8]]) + 0.7
    
        # 2 construct model
    
        # Defining model parameters using variables
        with tf.compat.v1.variable_scope("create_model"):
            weights = tf.Variable(initial_value=tf.compat.v1.random_normal(shape=[1, 1]), name="Weights")
            bias = tf.Variable(initial_value=tf.compat.v1.random_normal(shape=[1, 1]), name="Bias")
            y_predict = tf.matmul(x, weights) + bias
    
        # 3 construct loss function
        with tf.compat.v1.variable_scope("loss_function"):
            error = tf.reduce_mean(tf.square(y_predict - y_true))
    
        # 4 optimize loss function
        with tf.compat.v1.variable_scope("optimizer"):
            optimizer = tf.compat.v1.train.GradientDescentOptimizer(learning_rate=0.01).minimize(error)
    
        # 2++Collect variables
        tf.compat.v1.summary.scalar("error", error)
        tf.compat.v1.summary.histogram("weights", weights)
        tf.compat.v1.summary.histogram("bias", bias)
    
        # 3++Merge variables
        merged = tf.compat.v1.summary.merge_all()
    
        #Creating a saver object
        saver=tf.compat.v1.train.Saver()
    
        # initialize variable
        init = tf.compat.v1.global_variables_initializer()
    
        # Return call must be turned on to display results
        with tf.compat.v1.Session() as sess:
            sess.run(init)
    
            # 1++Create event file
            file_writer = tf.compat.v1.summary.FileWriter("./tmp/Linear", graph=sess.graph)
    
            # Query the value of initialization operation model parameters
            print("Before training, the model parameters were as follows:weight %f ,bias %f , loss %f " % (weights.eval(), bias.eval(), error.eval()))
    
            # Start training
            # for i in range(1000):
            #     sess.run(optimizer)
            #
            #     # Run merge variable operation
            #     summary = sess.run(merged)
            #
            #     # Write the data after each iteration to the event file
            #     file_writer.add_summary(summary, i)
            #
            #     #Save model
            #     if i % 10 == 0:
            #         saver.save(sess, "./tmp/model/my_linear.ckpt")
            #
            # print("After training, the model parameters were as follows:weight %f ,bias %f, loss %f " % (weights.eval(), bias.eval(), error.eval()))
    
            #Loading model
            if os.path.exists("./tmp/model/checkpoint"):
                saver.restore(sess, "./tmp/model/my_linear.ckpt")
            print("After training, the model parameters were as follows:weight %f ,bias %f, loss %f " % (weights.eval(), bias.eval(), error.eval()))
    
        return None
    
    
    if __name__ == "__main__":
        linear_regression()

     阶段8:命令行参数

    """
       __author__="dazhi"
        2021/3/6-21:28
    """
    import tensorflow as tf
    import os
    
    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
    
    
    # 1)Define command line parameters
    tf.compat.v1.app.flags.DEFINE_integer("max_step", 100, "Steps of training model")
    tf.compat.v1.app.flags.DEFINE_string("model_dir", "Unknown", "Path to save model + model name")
    
    # 2)Simplified variable name
    FLAGS = tf.compat.v1.app.flags.FLAGS
    
    def command_demo():
        """
        Demonstration of command line parameters
        :return:
        """
        print("max_step value :", FLAGS.max_step)
        print("model_dir value :", FLAGS.model_dir)
    
        return None
    
    
    
    if __name__ == "__main__":
        command_demo()

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  • 原文地址:https://www.cnblogs.com/dazhi151/p/14492011.html
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