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()