#!/usr/bin/python # -*- coding: UTF-8 -*- # @date: 2017/12/23 23:28 # @name: first_tf_1223 # @author:vickey-wu from __future__ import print_function import tensorflow as tf import os # disable error os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # constant node1 = tf.constant(3.0, dtype=tf.float32) node2 = tf.constant(4.0) # node2 dtype also equal tf.float32 implicitly print(node1, node2) # SSSession sess = tf.Session() # SSSession # placeholder a = tf.placeholder(tf.float32) # A placeholder is a promise to provide a value later b = tf.placeholder(tf.float32) adder_node = a + b print(sess.run(adder_node, {a: 3, b: 4.5})) # fetches=a, feed_dict=dict print(sess.run(adder_node, {a: [1, 3], b: [2, 4]})) # feed_dict=tuple # VVVariable W = tf.Variable([.3], dtype=tf.float32) b = tf.Variable([-.3], dtype=tf.float32) x = tf.placeholder(tf.float32) linear_model = W * x + b init = tf.global_variables_initializer() # tf.Variable must be explicitly initialize, tf.constant sess.run(init) print(sess.run(linear_model, {x: [1, 2, 3, 4]})) # while x=1, x=2, ... linear_model = ? # loss function to evaluate a model we build is good or not y = tf.placeholder(tf.float32) # desired values squared_deltas = tf.square(linear_model - y) # creates a vector of error delta loss = tf.reduce_sum(squared_deltas) # create a single scalar that abstracts the error of all examples print(sess.run(loss, {x: [1, 2, 3, 4], y: [0, -1, -2, -3]})) # manually reassign the values of W and b to get optimal solution of linear_model fixW = tf.assign(W, [-1.]) # tf.assign change initialized Variable value fixb = tf.assign(b, [1.]) sess.run([fixW, fixb]) print(sess.run(loss, {x: [1, 2, 3, 4], y: [0, -1, -2, -3]})) # tf.train API # machine learning is to find the correct model parameters automatically # TensorFlow provides optimizers that slowly change each variable in order to minimize the loss function # The simplest optimizer is gradient descent optimizer = tf.train.GradientDescentOptimizer(0.01) train = optimizer.minimize(loss) sess.run(init) for i in range(1000): sess.run(train, {x: [1, 2, 3, 4, ], y: [0, -1, -2, -3]}) print(sess.run([W, b])) ########################### # complete trainable linear regression model # model parameters W = tf.Variable([.3], dtype=tf.float32) b = tf.Variable([-.3], dtype=tf.float32) # model input and output x = tf.placeholder(tf.float32) y = tf.placeholder(tf.float32) linear_model = W * x + b # loss loss = tf.reduce_sum(tf.square(linear_model - y)) # optimizer optimizer = tf.train.GradientDescentOptimizer(0.01) train = optimizer.minimize(loss) # training data x_train = [1, 2, 3, 4] y_train = [0, -1, -2, -3] # training loop init = tf.global_variables_initializer() sess = tf.Session() sess.run(init) for i in range(1000): sess.run(train, {x: x_train, y: y_train}) # evaluate training accuracy curr_W, curr_b, curr_loss = sess.run([W, b, loss], {x: x_train, y: y_train}) print("W: %s b: %s loss: %s" % (curr_W, curr_b, curr_loss)) ######################### ########################## import numpy as np # import tensorflow as tf # Declare list of features feature_columns = [tf.feature_column.numeric_column("x", shape=[1])] # an estimator is the front end to invoke training and evaluation. estimator = tf.estimator.LinearRegressor(feature_columns=feature_columns) # tensorflow provides many helper method to read and set up data sets x_train = np.array([1., 2., 3., 4.]) y_train = np.array([0., -1., -2., -3.]) x_eval = np.array([2., 5., 8., 1.]) y_eval = np.array([-1.01, -4.1, -7, 0.]) input_fn = tf.estimator.inputs.numpy_input_fn( {"x": x_train}, y_train, batch_size=4, num_epochs=None, shuffle=True ) train_input_fn = tf.estimator.inputs.numpy_input_fn( {"x": x_train}, y_train, batch_size=4, num_epochs=1000, shuffle=False ) eval_input_fn = tf.estimator.inputs.numpy_input_fn( {"x": x_train}, y_train, batch_size=4, num_epochs=1000, shuffle=False ) # we can invoke 1000 training steps by invoking the method and passing the training data set. estimator.train(input_fn=input_fn, steps=1000) # Here we evaluate how well our model did. train_metrics = estimator.evaluate(input_fn=train_input_fn) eval_metrics = estimator.evaluate(input_fn=eval_input_fn) print("train metrics: %r" % train_metrics) print("eval metrics: %r" % eval_metrics) #######################
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