Verify the value of a tensor in a function - tensorflow

Question

I am creating a deep learning fully connected NN for the MNIST input. I have a function (it takes placeholder input)
# Create model
def multilayer_perceptron(x, activation_fn, weights, biases, dbg=False):
layerDatas = OrderedDict()
# get each layer data
prev = x
for i in range(len(weights)-1):
weight = weights.items()[i][1]
bias = biases.items()[i][1]
var = 'layer_' + str(i+1)
layerData = tf.add(tf.matmul(prev, weight), bias)
layerData = activation_fn(layerData)
prev = layerData
layerDatas[var] = layerData
# output layer with linear function, using the last layer output value
val = tf.matmul(prev, weights['out'])
out_layer = tf.matmul(prev, weights['out']) + biases['out']
print x.eval() # debug the data
return out_layer
which takes multiple layers in weights and biases. I call the main program with
sess = tf.InteractiveSession() # start a session
print 'Data', n_input, n_classes
print 'Train', train_set_x.shape, train_set_y.shape
(weights, biases) = createWeightsBiases(layers, n_input, n_classes, dbg)
# tf Graph input
x = tf.placeholder("float", [None, n_input])
y = tf.placeholder("float", [None, n_classes])
# Construct model
pred = multilayer_perceptron(x, activation_fn, weights, biases, dbg)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
# Initializing the variables
init = tf.initialize_all_variables()
done_looping = False
display_step = 1
# Add ops to save and restore all the variables.
saver = tf.train.Saver()
# Launch the graph
sess.run(init)
# Training cycle
epochs = 1000
for epoch in range(epochs):
avg_cost = 0.
total_batch = int(len(train_set_x)/batch_size)
print 'Batch', total_batch, batch_size
# Loop over all batches
for i in range(total_batch):
batch_x = train_set_x[i * batch_size: (i + 1) * batch_size]
batch_y = train_set_y[i * batch_size: (i + 1) * batch_size]
# Run optimization op (backprop) and cost op (to get loss value)
_, c = sess.run([optimizer, cost], feed_dict={x: batch_x,
y: batch_y})
# Compute average loss
avg_cost += c / total_batch
# Display logs per epoch step
if epoch % display_step == 0:
print "Epoch:", '%04d' % (epoch+1), "cost=", \
"{:.9f}".format(avg_cost)
print "Optimization Finished!"
# Test model
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
# Calculate accuracy
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
print "Accuracy:", accuracy.eval({x: valid_set_x, y: valid_set_y})
When I try to print the tensor in my multilayer_perceptron function, I get a crash with
tensorflow.python.framework.errors.InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder' with dtype float
[[Node: Placeholder = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
I would appreciate help in getting around this.

Answer 1

You can't eval a placeholder. Instead, you can feed the graph with a proper value for the placeholder and only then extract the content (that's the value you fed the graph with).
So, remove the print x.eval() # debug the data line from the multilayer_perceptron function.
To inspect the value of the placeholder you have to fed it and extract the value you just feed it (side note: it's useless).
If you really want to do this, that's how:
placeholder_value = sess.run(x, feed_dict={x: [1,2,3,4]})
print placeholder_value
It will print the value [1,2,3,4]