Tf.Print() doesn't print the shape of the tensors? - tensorflow

Question

I have written a simple classification program using Tensorflow and getting the output except I tried to print the shape of tensors for Model parameters, features & bias.
The function definations:
import tensorflow as tf, numpy as np
from tensorflow.examples.tutorials.mnist import input_data
def get_weights(n_features, n_labels):
# Return weights
return tf.Variable( tf.truncated_normal((n_features, n_labels)) )
def get_biases(n_labels):
# Return biases
return tf.Variable( tf.zeros(n_labels))
def linear(input, w, b):
# Linear Function (xW + b)
# return np.dot(input,w) + b
return tf.add(tf.matmul(input,w), b)
def mnist_features_labels(n_labels):
"""Gets the first <n> labels from the MNIST dataset
"""
mnist_features = []
mnist_labels = []
mnist = input_data.read_data_sets('dataset/mnist', one_hot=True)
# In order to make quizzes run faster, we're only looking at 10000 images
for mnist_feature, mnist_label in zip(*mnist.train.next_batch(10000)):
# Add features and labels if it's for the first <n>th labels
if mnist_label[:n_labels].any():
mnist_features.append(mnist_feature)
mnist_labels.append(mnist_label[:n_labels])
return mnist_features, mnist_labels
The graph creation :
# Number of features (28*28 image is 784 features)
n_features = 784
# Number of labels
n_labels = 3
# Features and Labels
features = tf.placeholder(tf.float32)
labels = tf.placeholder(tf.float32)
# Weights and Biases
w = get_weights(n_features, n_labels)
b = get_biases(n_labels)
# Linear Function xW + b
logits = linear(features, w, b)
# Training data
train_features, train_labels = mnist_features_labels(n_labels)
print("Total {0} data points of Training Data, each having {1} features \n \
Total {2} number of labels,each having 1-hot encoding {3}".format(len(train_features),len(train_features[0]),\
len(train_labels),train_labels[0]
)
)
# global variables initialiser
init= tf.global_variables_initializer()
with tf.Session() as session:
session.run(init)
The problem is here :
# shapes =tf.Print ( tf.shape(features), [tf.shape(features),
# tf.shape(labels),
# tf.shape(w),
# tf.shape(b),
# tf.shape(logits)
# ], message= "The shapes are:" )
# print("Verify shapes",shapes)
logits = tf.Print(logits, [tf.shape(features),
tf.shape(labels),
tf.shape(w),
tf.shape(b),
tf.shape(logits)],
message= "The shapes are:")
print(logits)
I looked at here, but didn't find much useful.
# Softmax
prediction = tf.nn.softmax(logits)
# Cross entropy
# This quantifies how far off the predictions were.
# You'll learn more about this in future lessons.
cross_entropy = -tf.reduce_sum(labels * tf.log(prediction), reduction_indices=1)
# Training loss
# You'll learn more about this in future lessons.
loss = tf.reduce_mean(cross_entropy)
# Rate at which the weights are changed
# You'll learn more about this in future lessons.
learning_rate = 0.08
# Gradient Descent
# This is the method used to train the model
# You'll learn more about this in future lessons.
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)
# Run optimizer and get loss
_, l = session.run(
[optimizer, loss],
feed_dict={features: train_features, labels: train_labels})
# Print loss
print('Loss: {}'.format(l))
The output I am getting is :
Extracting dataset/mnist/train-images-idx3-ubyte.gz
Extracting dataset/mnist/train-labels-idx1-ubyte.gz
Extracting dataset/mnist/t10k-images-idx3-ubyte.gz
Extracting dataset/mnist/t10k-labels-idx1-ubyte.gz
Total 3118 data points of Training Data, each having 784 features
Total 3118 number of labels,each having 1-hot encoding [0. 1. 0.]
Tensor("Print_22:0", shape=(?, 3), dtype=float32)
Loss: 5.339271068572998
Could anyone help me understand, Why I am not able to see the shapes of the tensors?

Answer 1

That is not how you use tf.Print. It is an op that does nothing on its own (simply returns the input) but prints the requested tensors as a side effect. You should do something like
logits = tf.Print(logits, [tf.shape(features),
tf.shape(labels),
tf.shape(w),
tf.shape(b),
tf.shape(logits)],
message= "The shapes are:")
Now, whenever logits is evaluated (as it will be for computing the loss/gradients), the shape information will be printed.
What you are doing right now is simply printing the return value of the tf.Print op, which is just its input (tf.shape(features)).

Answer 2

After #xdurch0 suggestion, I tried this
shapes = tf.Print(logits, [tf.shape(features),
tf.shape(labels),
tf.shape(w),
tf.shape(b),
tf.shape(logits)],
message= "The shapes are:")
# Run optimizer and get loss
_, l, resultingShapes = session.run( [optimizer, loss, shapes],
feed_dict={features: train_features, labels: train_labels})
print('The shapes are: '. resultingShapes.shape)
and it worked partially,
Extracting dataset/mnist/train-images-idx3-ubyte.gz
Extracting dataset/mnist/train-labels-idx1-ubyte.gz
Extracting dataset/mnist/t10k-images-idx3-ubyte.gz
Extracting dataset/mnist/t10k-labels-idx1-ubyte.gz
Total 3118 data points of Training Data, each having 784 features
Total 3118 number of labels, each having 1-hot encoding [0. 1. 0.]
The shapes are: (3118, 3)
Loss: 10.223002433776855
could #xdurch0 suggest something to get the desired results?
My DESIRED RESULTS are:
tf.shape(features): (3118, 784) tf.shape(labels) :(3118, 3) ,
tf.shape(w) : (784,3), tf.shape(b) : (3,1), tf.shape(logits):(3118,3)