tensorflow_cookbook：Ch 1: Getting Started with TensorFlow_(3,4)

Variables and Placeholders

Here we initialize a simple placeholder for a piece of data. We then feed a random number into an identity function. If viewed in Tensorboard, the following graph will be created.

#03_placeholders.py# Placeholders#----------------------------------## This function introduces how to # use placeholders in Tensorflowimport numpy as npimport tensorflow as tffrom tensorflow.python.framework import opsops.reset_default_graph()# Using Placeholderssess = tf.Session()x = tf.placeholder(tf.float32, shape=(4, 4))y = tf.identity(x)rand_array = np.random.rand(4, 4)#####merged = tf.merge_all_summaries()merged = tf.summary.merge_all()#writer = tf.train.SummaryWriter("/tmp/variable_logs", sess.graph_def)writer = tf.summary.FileWriter("/tmp/variable_logs", sess.graph)####writer.close()print(sess.run(y, feed_dict={x: rand_array}))

原代码会报错

AttributeError: module 'tensorflow' has no attribute 'merge_all_summaries'AttributeError: module 'tensorflow.python.training.training' has no attribute 'SummaryWriter'

修改后结果

[[ 0.66682321  0.3293488   0.5245204   0.53379935] [ 0.07488234  0.10959593  0.68837297  0.17703928] [ 0.35069257  0.77648026  0.27833292  0.44115242] [ 0.76638317  0.15466152  0.22070079  0.94068062]]

Working with Matrices

Placeholder for future purposes.

# 04_matrices.py#Matrices and Matrix Operations#----------------------------------## This function introduces various ways to create# matrices and how to use them in Tensorflowimport numpy as npimport tensorflow as tffrom tensorflow.python.framework import opsops.reset_default_graph()# Declaring matricessess = tf.Session()# Declaring matrices# Identity matrixidentity_matrix = tf.diag([1.0,1.0,1.0])print(sess.run(identity_matrix))# 2x3 random norm matrixA = tf.truncated_normal([2,3])print(sess.run(A))# 2x3 constant matrixB = tf.fill([2,3], 5.0)print(sess.run(B))# 3x2 random uniform matrixC = tf.random_uniform([3,2])print(sess.run(C))print(sess.run(C)) # Note that we are reinitializing, hence the new random variabels# Create matrix from np arrayD = tf.convert_to_tensor(np.array([[1., 2., 3.], [-3., -7., -1.], [0., 5., -2.]]))print(sess.run(D))# Matrix addition/subtractionprint(sess.run(A+B))print(sess.run(B-B))# Matrix Multiplicationprint(sess.run(tf.matmul(B, identity_matrix)))# Matrix Transposeprint(sess.run(tf.transpose(C))) # Again, new random variables# Matrix Determinantprint(sess.run(tf.matrix_determinant(D)))# Matrix Inverseprint(sess.run(tf.matrix_inverse(D)))# Cholesky Decompositionprint(sess.run(tf.cholesky(identity_matrix)))# Eigenvalues and Eigenvectorsprint(sess.run(tf.self_adjoint_eig(D)))

[[ 1.  0.  0.] [ 0.  1.  0.] [ 0.  0.  1.]][[ 0.4898375  -0.65810621  0.20719798] [-0.45387158 -0.72594666  0.2478656 ]][[ 5.  5.  5.] [ 5.  5.  5.]][[ 0.37838769  0.72760344] [ 0.2227149   0.2249614 ] [ 0.73352134  0.32676136]][[ 0.90364814  0.73233008] [ 0.61350799  0.37857103] [ 0.41849363  0.42342687]][[ 1.  2.  3.] [-3. -7. -1.] [ 0.  5. -2.]][[ 3.90597057  4.19510365  6.0628376 ] [ 4.86557484  6.02541924  3.63600206]][[ 0.  0.  0.] [ 0.  0.  0.]][[ 5.  5.  5.] [ 5.  5.  5.]][[ 0.04690075  0.34524703  0.48222351] [ 0.15054452  0.74442649  0.36477065]]-38.0[[-0.5        -0.5        -0.5       ] [ 0.15789474  0.05263158  0.21052632] [ 0.39473684  0.13157895  0.02631579]][[ 1.  0.  0.] [ 0.  1.  0.] [ 0.  0.  1.]](array([-10.65907521,  -0.22750691,   2.88658212]), array([[ 0.21749542,  0.63250104, -0.74339638],       [ 0.84526515,  0.2587998 ,  0.46749277],       [-0.4880805 ,  0.73004459,  0.47834331]]))