Ubuntu installation TensorFlow and its minist instances

1vmware install Ubuntu (oneself follow other tutorial easy to install Ubuntu, then install TensorFlow always error), follow this step follow TensorFlow step successful click Open Link 2 ubuntu install TensorFlow (CPU) Use PIP to click Open Link 3minist data set test, but will error, follow the online method changed after correct run click Open Link a softmax modified code (placed in the desktop test.py file)

From tensorflow.examples.tutorials.mnist import input_data import tensorflow as TF # import data Mnist = Input_data.rea D_data_sets (' Mnist_data ', one_hot=true) # Create the Model X = Tf.placeholder (Tf.float32, [None, 784]) W = tf. Variable (Tf.zeros ([784, ten])) B = tf. Variable (Tf.zeros ([ten])) y = Tf.matmul (x, W) + B # Define loss and Optimizer y_ = Tf.placeholder (Tf.float32, [None, 10] ) Cross_entropy = Tf.reduce_mean (Tf.nn.softmax_cross_entropy_with_logits (Labels=y_, logits=y)) Train_step = Tf.tra In. Gradientdescentoptimizer (0.5). Minimize (cross_entropy) Sess = tf. InteractiveSession () Tf.initialize_all_variables (). Run () # Train for _ in range: Batch_xs, Batch_ys = mnist.tr Ain.next_batch (Sess.run) (Train_step, Feed_dict={x:batch_xs, Y_: Batch_ys}) # Test trained model CORRECT_PREDICTI On = Tf.equal (Tf.argmax (y, 1), Tf.argmax (Y_, 1)) accuracy = Tf.reduce_mean (Tf.cast (correct_prediction, tf.float32)) Prin T (Sess.run (accuracy, Feed_dict={x:mnist.test.imaGES, Y_: Mnist.test.labels})) 

Run result b convolutional network modified code (named testcon.py)

From tensorflow.examples.tutorials.mnist import input_data import tensorflow as TF # import data Mnist = Input_data.read_d Ata_sets (' Mnist_data ', one_hot=true) x = Tf.placeholder (Tf.float32, [None, 784]) Y_ = Tf.placeholder (Tf.float32, [None, 1 0]) Sess = tf. InteractiveSession () def weight_variable (shape): initial = Tf.truncated_normal (shape, stddev=0.1) return TF. Variable (initial) def bias_variable (shape): initial = Tf.constant (0.1, Shape=shape) return TF. Variable (initial) def conv2d (X, W): Return tf.nn.conv2d (x, W, strides=[1, 1, 1, 1], padding= ' same ') def max_pool_2x2 (x) : Return Tf.nn.max_pool (x, Ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding= ' same ') W_conv1 = W Eight_variable ([5, 5, 1, +]) B_conv1 = bias_variable ([+]) X_image = Tf.reshape (x, [ -1,28,28,1]) h_conv1 = Tf.nn.relu (con V2d (X_image, W_CONV1) + b_conv1) H_pool1 = max_pool_2x2 (h_conv1) w_conv2 = Weight_variable ([5, 5, (+)]) B_conv2 = Bias_ Variable ([+]) H_conv2 = Tf.nn.relu (conv2d (H_pool1, W_conv2) + b_conv2) H_pool2 = max_pool_2x2 (h_conv2) w_fc1 = weight_variable ([7 * 7 *, 1024x768]) B_FC1 = Bias_vari
Able ([1024x768]) H_pool2_flat = Tf.reshape (H_pool2, [-1, 7*7*64]) H_fc1 = Tf.nn.relu (Tf.matmul (H_pool2_flat, W_FC1) + b_fc1) Keep_prob = Tf.placeholder (tf.float32) H_fc1_drop = Tf.nn.dropout (H_FC1, keep_prob) W_FC2 = Weight_variable ([1024x768, ten]) b _FC2 = Bias_variable ([ten]) Y_conv = Tf.matmul (H_fc1_drop, W_FC2) + b_fc2 cross_entropy = Tf.reduce_mean (Tf.nn.softma X_cross_entropy_with_logits (Labels=y_, logits=y_conv)) Train_step = Tf.train.AdamOptimizer (1e-4). Minimize (Cross_ Entropy) Correct_prediction = Tf.equal (Tf.argmax (y_conv,1), Tf.argmax (y_,1)) accuracy = Tf.reduce_mean (Tf.cast ( Correct_prediction, Tf.float32)) Sess.run (Tf.initialize_all_variables ()) for I in Range (20000): Batch = Mnist.train.nex T_batch () if i%100 = = 0:train_accuracy = Accuracy.eval (feed_dict={x:batch[0], Y_: batch[1], keep_prob:1 .0}) Print ("Step%d, training accuracy%g"%(i, train_accuracy)) Train_step.run (Feed_dict={x:batch[0], Y_: batch[1], keep_prob:0.5}) print ("Test accuracy%g"%accuracy.eval (feed_dict ={x:mnist.test.images, Y_: Mnist.test.labels, keep_prob:1.0}))

Run results