Tensorflow安装与测试

安装、

Ubuntu/Linux 64-bit

$ sudo apt-get install python-pip python-dev

Ubuntu/Linux 64-bit, CPU only, Python 2.7

$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.12.0rc0-cp27-none-linux_x86_64.whl

Python 2

$  sudo pip install --upgrade $TF_BINARY_URL

Python 3

$ sudo pip3 install --upgrade $TF_BINARY_URL

测试一、

$ python...>>> import tensorflow as tf>>> hello = tf.constant('Hello, TensorFlow!')>>> sess = tf.Session()>>> print(sess.run(hello))Hello, TensorFlow!>>> a = tf.constant(10)>>> b = tf.constant(32)>>> print(sess.run(a + b))42>>>

测试二、

import tensorflow as tfimport numpyimport matplotlib.pyplot as pltrng = numpy.randomlearning_rate = 0.01training_epochs = 1000display_step = 50#数据集xtrain_X = numpy.asarray([3.3,4.4,5.5,7.997,5.654,.71,6.93,4.168,9.779,6.182,7.59,2.167,                         7.042,10.791,5.313,9.27,3.1])#数据集ytrain_Y = numpy.asarray([1.7,2.76,3.366,2.596,2.53,1.221,1.694,1.573,3.465,1.65,2.09,                         2.827,3.19,2.904,2.42,2.94,1.3])n_samples = train_X.shape[0]X = tf.placeholder("float")Y = tf.placeholder("float")W = tf.Variable(rng.randn(), name="weight")b = tf.Variable(rng.randn(), name="bias")pred = tf.add(tf.mul(X, W), b)cost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples)optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)init = tf.initialize_all_variables()with tf.Session() as sess:    sess.run(init)    # 训练数据    for epoch in range(training_epochs):        for (x, y) in zip(train_X, train_Y):            sess.run(optimizer, feed_dict={X: x, Y: y})    print "优化完成!"    training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y})    print "Training cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n'    #可视化显示    plt.plot(train_X, train_Y, 'ro', label='Original data')    plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')    plt.legend()    plt.show()

测试二效果：

更多技术干货请关注：