TensorFlow安装并在Pycharm搭建环境的详细图文教程

TensorFlow是目前最受欢迎的机器学习框架之一，它支持Python等多种编程语言，也可以在CPU和GPU上运行。在Pycharm上搭建TensorFlow环境可以更方便的进行开发。下面是一份详细的TensorFlow安装并在Pycharm搭建环境的攻略。

1. 安装Anaconda

Anaconda是一个包含Python解释器、数据科学库以及许多实用工具的软件分发。它可以方便地进行Python环境安装、包管理和环境管理等。在官网https://www.anaconda.com 下载相应操作系统安装程序，按照默认选项进行安装即可。

2. 安装TensorFlow

在Anaconda环境中，可以使用conda安装TensorFlow。打开Anaconda Prompt（Windows）或Terminal（Mac或Linux），输入以下命令：

conda create -n tensorflow python=3.6

此命令将创建一个名为tensorflow的Python 3.6环境。

接下来，通过安装TensorFlow包来安装TensorFlow：

conda activate tensorflow
conda install tensorflow

3. 安装Pycharm

在官网 https://www.jetbrains.com/pycharm/ 下载相应操作系统安装程序，按照默认选项进行安装即可。

4. 配置TensorFlow环境

在Pycharm中，点击Create New Project(创建新项目)选项，填写项目名称和路径信息，选择刚刚创建的TensorFlow环境，点击Create(创建)按钮。完成之后，可以看到Pycharm在新建项目时会自动激活TensorFlow环境。

5. 构建一个TensorFlow应用程序

在Pycharm中打开新建项目，在菜单栏中选择File->New Python File，输入文件名和路径信息，编写以下Python程序来测试TensorFlow：

import tensorflow as tf
hello = tf.constant('Hello, TensorFlow!')
sess = tf.Session()
print(sess.run(hello))

点击运行按钮即可看到输出结果。

示例说明

示例一：使用TensorFlow实现线性回归

下面是一个使用TensorFlow实现线性回归的示例，一个简单的线性回归模型：y = w*x + b

import tensorflow as tf
import numpy as np

# Generate data
n_samples = 1000
x_data = np.random.normal(0.0, 0.1, size=[n_samples, 1])
noise = np.random.normal(0.0, 0.1, size=[n_samples, 1])
y_data = x_data * 0.5 + 0.2 + noise

# Create TensorFlow inputs
x = tf.placeholder(tf.float32, shape=[None, 1], name='x')
y = tf.placeholder(tf.float32, shape=[None, 1], name='y')

# Create TensorFlow variables
w = tf.Variable(tf.random_normal(shape=[1]), name='weights')
b = tf.Variable(tf.zeros(shape=[1]), name='biases')

# Create TensorFlow operations to calculate the prediction
y_pred = tf.matmul(x, tf.reshape(w, [1, 1])) + b

# Create TensorFlow operation to calculate the loss
loss = tf.reduce_mean(tf.square(y - y_pred))

# Create TensorFlow optimizer
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.5)
train_op = optimizer.minimize(loss)

# Create TensorFlow session
with tf.Session() as sess:
    # Initialize variables
    sess.run(tf.global_variables_initializer())

    # Train loop
    for i in range(100):
        # Run the train operation with data
        _, loss_value = sess.run([train_op, loss], feed_dict={x: x_data, y: y_data})
        if i % 10 == 0:
            print('Step: {}, Loss: {}'.format(i, loss_value))

此示例展示了如何使用TensorFlow创建一个简单的线性回归模型，以及如何使用梯度下降算法来训练模型。

示例二：使用TensorFlow实现卷积神经网络

下面是一个使用TensorFlow实现卷积神经网络的示例，实现图像分类：

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

# Download and load MNIST dataset
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

# Reshape and scale data
x_train = np.expand_dims(x_train, axis=-1) / 255.0
x_test = np.expand_dims(x_test, axis=-1) / 255.0

# Create TensorFlow inputs
x = tf.placeholder(tf.float32, shape=[None, 28, 28, 1], name='x')
y = tf.placeholder(tf.int32, shape=[None], name='y')

# Create TensorFlow layers
conv1 = tf.layers.conv2d(inputs=x, filters=32, kernel_size=[5, 5], padding='same', activation=tf.nn.relu)
pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)
conv2 = tf.layers.conv2d(inputs=pool1, filters=64, kernel_size=[5, 5], padding='same', activation=tf.nn.relu)
pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)
flatten = tf.layers.flatten(inputs=pool2)
dense = tf.layers.dense(inputs=flatten, units=1024, activation=tf.nn.relu)
logits = tf.layers.dense(inputs=dense, units=10)

# Create TensorFlow operation to calculate the loss
one_hot_y = tf.one_hot(indices=y, depth=10)
cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(labels=one_hot_y, logits=logits)
loss = tf.reduce_mean(cross_entropy)

# Create TensorFlow optimizer
optimizer = tf.train.AdamOptimizer()
train_op = optimizer.minimize(loss)

# Create TensorFlow session
with tf.Session() as sess:
    # Initialize variables
    sess.run(tf.global_variables_initializer())

    # Train loop
    batch_size = 128
    num_examples = x_train.shape[0]
    for i in range(1, 11):
        # Shuffle data
        perm = np.random.permutation(num_examples)
        x_train, y_train = x_train[perm], y_train[perm]

        # Train on batches
        for offset in range(0, num_examples, batch_size):
            end = offset + batch_size
            batch_x, batch_y = x_train[offset:end], y_train[offset:end]
            _, loss_value = sess.run([train_op, loss], feed_dict={x: batch_x, y: batch_y})

        # Test on test set
        accuracy = sess.run(
            tf.reduce_mean(tf.cast(tf.equal(tf.argmax(logits, 1), y), tf.float32)),
            feed_dict={x: x_test, y: y_test})

        print('Epoch: {}, Loss: {:.4f}, Accuracy: {:.4f}'.format(i, loss_value, accuracy))

此示例展示了如何使用TensorFlow创建一个卷积神经网络模型，并在MNIST数据集上进行图像分类。