undestanding feed_dict in sess.run

Question

I'm new to tensorflow. I have some code I'm trying to understand. Is there a way to get a list of all possible inputs for the "feed_dict" in sess.run? Is the structure of feed_dict always the same or does it depend on the session?

code:

sess.run([input,input2],feed_dict={is_train:False,y:stuff,user:[_user]})

Update:

Code below from comments describing how to get input for feed_dict

Code:

# populate session graph to look at place holders
# place holders are possible inputs to sess.run()

for op in sess.graph.get_operations():
     print(op.name, op.type)

Output:

(u'Placeholder', u'Placeholder')
(u'ToFloat', u'Cast')
(u'sub/y', u'Const')
(u'sub', u'Sub')
(u'div/y', u'Const')
(u'div', u'RealDiv')
(u'Placeholder_1', u'Placeholder')
(u'DVBPR/Reshape/shape', u'Const')
(u'DVBPR/Reshape', u'Reshape')
(u'DVBPR/wc1/Initializer/random_uniform/shape', u'Const')
(u'DVBPR/wc1/Initializer/random_uniform/min', u'Const')
(u'DVBPR/wc1/Initializer/random_uniform/max', u'Const')
(u'DVBPR/wc1/Initializer/random_uniform/RandomUniform', u'RandomUniform')
(u'DVBPR/wc1/Initializer/random_uniform/sub', u'Sub')
(u'DVBPR/wc1/Initializer/random_uniform/mul', u'Mul')
(u'DVBPR/wc1/Initializer/random_uniform', u'Add')
(u'DVBPR/wc1', u'VariableV2')
(u'DVBPR/wc1/Assign', u'Assign')
(u'DVBPR/wc1/read', u'Identity')
(u'DVBPR/zeros', u'Const')
(u'DVBPR/bc1', u'VariableV2')
(u'DVBPR/bc1/Assign', u'Assign')
(u'DVBPR/bc1/read', u'Identity')
(u'DVBPR/Conv2D', u'Conv2D')
(u'DVBPR/BiasAdd', u'BiasAdd')
(u'DVBPR/Relu', u'Relu')
(u'DVBPR/Relu_1', u'Relu')
(u'DVBPR/MaxPool', u'MaxPool')
(u'DVBPR/wc2/Initializer/random_uniform/shape', u'Const')
(u'DVBPR/wc2/Initializer/random_uniform/min', u'Const')
(u'DVBPR/wc2/Initializer/random_uniform/max', u'Const')
(u'DVBPR/wc2/Initializer/random_uniform/RandomUniform', u'RandomUniform')
(u'DVBPR/wc2/Initializer/random_uniform/sub', u'Sub')
(u'DVBPR/wc2/Initializer/random_uniform/mul', u'Mul')
(u'DVBPR/wc2/Initializer/random_uniform', u'Add')
(u'DVBPR/wc2', u'VariableV2')
(u'DVBPR/wc2/Assign', u'Assign')
(u'DVBPR/wc2/read', u'Identity')
(u'DVBPR/zeros_1', u'Const')
(u'DVBPR/bc2', u'VariableV2')
(u'DVBPR/bc2/Assign', u'Assign')
(u'DVBPR/bc2/read', u'Identity')
(u'DVBPR/Conv2D_1', u'Conv2D')
(u'DVBPR/BiasAdd_1', u'BiasAdd')
(u'DVBPR/Relu_2', u'Relu')
(u'DVBPR/Relu_3', u'Relu')
(u'DVBPR/MaxPool_1', u'MaxPool')
(u'DVBPR/wc3/Initializer/random_uniform/shape', u'Const')
(u'DVBPR/wc3/Initializer/random_uniform/min', u'Const')
(u'DVBPR/wc3/Initializer/random_uniform/max', u'Const')
(u'DVBPR/wc3/Initializer/random_uniform/RandomUniform', u'RandomUniform')
(u'DVBPR/wc3/Initializer/random_uniform/sub', u'Sub')
(u'DVBPR/wc3/Initializer/random_uniform/mul', u'Mul')
(u'DVBPR/wc3/Initializer/random_uniform', u'Add')
(u'DVBPR/wc3', u'VariableV2')
(u'DVBPR/wc3/Assign', u'Assign')
(u'DVBPR/wc3/read', u'Identity')
(u'DVBPR/zeros_2', u'Const')
(u'DVBPR/bc3', u'VariableV2')
(u'DVBPR/bc3/Assign', u'Assign')
(u'DVBPR/bc3/read', u'Identity')
(u'DVBPR/Conv2D_2', u'Conv2D')
(u'DVBPR/BiasAdd_2', u'BiasAdd')
(u'DVBPR/Relu_4', u'Relu')
(u'DVBPR/Relu_5', u'Relu')
(u'DVBPR/wc4/Initializer/random_uniform/shape', u'Const')
(u'DVBPR/wc4/Initializer/random_uniform/min', u'Const')
(u'DVBPR/wc4/Initializer/random_uniform/max', u'Const')
(u'DVBPR/wc4/Initializer/random_uniform/RandomUniform', u'RandomUniform')
(u'DVBPR/wc4/Initializer/random_uniform/sub', u'Sub')
(u'DVBPR/wc4/Initializer/random_uniform/mul', u'Mul')
(u'DVBPR/wc4/Initializer/random_uniform', u'Add')
(u'DVBPR/wc4', u'VariableV2')
(u'DVBPR/wc4/Assign', u'Assign')
(u'DVBPR/wc4/read', u'Identity')
(u'DVBPR/zeros_3', u'Const')
(u'DVBPR/bc4', u'VariableV2')
(u'DVBPR/bc4/Assign', u'Assign')
(u'DVBPR/bc4/read', u'Identity')
(u'DVBPR/Conv2D_3', u'Conv2D')
(u'DVBPR/BiasAdd_3', u'BiasAdd')
(u'DVBPR/Relu_6', u'Relu')
(u'DVBPR/Relu_7', u'Relu')
(u'DVBPR/wc5/Initializer/random_uniform/shape', u'Const')
(u'DVBPR/wc5/Initializer/random_uniform/min', u'Const')
(u'DVBPR/wc5/Initializer/random_uniform/max', u'Const')
(u'DVBPR/wc5/Initializer/random_uniform/RandomUniform', u'RandomUniform')
(u'DVBPR/wc5/Initializer/random_uniform/sub', u'Sub')
(u'DVBPR/wc5/Initializer/random_uniform/mul', u'Mul')
(u'DVBPR/wc5/Initializer/random_uniform', u'Add')
(u'DVBPR/wc5', u'VariableV2')
(u'DVBPR/wc5/Assign', u'Assign')
(u'DVBPR/wc5/read', u'Identity')
(u'DVBPR/zeros_4', u'Const')
(u'DVBPR/bc5', u'VariableV2')
(u'DVBPR/bc5/Assign', u'Assign')
(u'DVBPR/bc5/read', u'Identity')
(u'DVBPR/Conv2D_4', u'Conv2D')
(u'DVBPR/BiasAdd_4', u'BiasAdd')
(u'DVBPR/Relu_8', u'Relu')
(u'DVBPR/Relu_9', u'Relu')
(u'DVBPR/MaxPool_2', u'MaxPool')
(u'DVBPR/Reshape_1/shape', u'Const')
(u'DVBPR/Reshape_1', u'Reshape')
(u'DVBPR/wd1/Initializer/random_uniform/shape', u'Const')
(u'DVBPR/wd1/Initializer/random_uniform/min', u'Const')
(u'DVBPR/wd1/Initializer/random_uniform/max', u'Const')
(u'DVBPR/wd1/Initializer/random_uniform/RandomUniform', u'RandomUniform')
(u'DVBPR/wd1/Initializer/random_uniform/sub', u'Sub')
(u'DVBPR/wd1/Initializer/random_uniform/mul', u'Mul')
(u'DVBPR/wd1/Initializer/random_uniform', u'Add')
(u'DVBPR/wd1', u'VariableV2')
(u'DVBPR/wd1/Assign', u'Assign')
(u'DVBPR/wd1/read', u'Identity')
(u'DVBPR/MatMul', u'MatMul')
(u'DVBPR/zeros_5/shape_as_tensor', u'Const')
(u'DVBPR/zeros_5/Const', u'Const')
(u'DVBPR/zeros_5', u'Fill')
(u'DVBPR/bd1', u'VariableV2')
(u'DVBPR/bd1/Assign', u'Assign')
(u'DVBPR/bd1/read', u'Identity')
(u'DVBPR/Add', u'Add')
(u'DVBPR/Relu_10', u'Relu')
(u'DVBPR/dropout/keep_prob', u'Const')
(u'DVBPR/wd2/Initializer/random_uniform/shape', u'Const')
(u'DVBPR/wd2/Initializer/random_uniform/min', u'Const')
(u'DVBPR/wd2/Initializer/random_uniform/max', u'Const')
(u'DVBPR/wd2/Initializer/random_uniform/RandomUniform', u'RandomUniform')
(u'DVBPR/wd2/Initializer/random_uniform/sub', u'Sub')
(u'DVBPR/wd2/Initializer/random_uniform/mul', u'Mul')
(u'DVBPR/wd2/Initializer/random_uniform', u'Add')
(u'DVBPR/wd2', u'VariableV2')
(u'DVBPR/wd2/Assign', u'Assign')
(u'DVBPR/wd2/read', u'Identity')
(u'DVBPR/MatMul_1', u'MatMul')
(u'DVBPR/zeros_6/shape_as_tensor', u'Const')
(u'DVBPR/zeros_6/Const', u'Const')
(u'DVBPR/zeros_6', u'Fill')
(u'DVBPR/bd2', u'VariableV2')
(u'DVBPR/bd2/Assign', u'Assign')
(u'DVBPR/bd2/read', u'Identity')
(u'DVBPR/Add_1', u'Add')
(u'DVBPR/Relu_11', u'Relu')
(u'DVBPR/dropout_1/keep_prob', u'Const')
(u'DVBPR/wd3/Initializer/random_uniform/shape', u'Const')
(u'DVBPR/wd3/Initializer/random_uniform/min', u'Const')
(u'DVBPR/wd3/Initializer/random_uniform/max', u'Const')
(u'DVBPR/wd3/Initializer/random_uniform/RandomUniform', u'RandomUniform')
(u'DVBPR/wd3/Initializer/random_uniform/sub', u'Sub')
(u'DVBPR/wd3/Initializer/random_uniform/mul', u'Mul')
(u'DVBPR/wd3/Initializer/random_uniform', u'Add')
(u'DVBPR/wd3', u'VariableV2')
(u'DVBPR/wd3/Assign', u'Assign')
(u'DVBPR/wd3/read', u'Identity')
(u'DVBPR/MatMul_2', u'MatMul')
(u'DVBPR/zeros_7', u'Const')
(u'DVBPR/bd3', u'VariableV2')
(u'DVBPR/bd3/Assign', u'Assign')
(u'DVBPR/bd3/read', u'Identity')
(u'DVBPR/Add_2', u'Add')
(u'DVBPR/random_uniform/shape', u'Const')
(u'DVBPR/random_uniform/min', u'Const')
(u'DVBPR/random_uniform/max', u'Const')
(u'DVBPR/random_uniform/RandomUniform', u'RandomUniform')
(u'DVBPR/random_uniform/sub', u'Sub')
(u'DVBPR/random_uniform/mul', u'Mul')
(u'DVBPR/random_uniform', u'Add')
(u'DVBPR/div/y', u'Const')
(u'DVBPR/div', u'RealDiv')
(u'DVBPR/Variable', u'VariableV2')
(u'DVBPR/Variable/Assign', u'Assign')
(u'DVBPR/Variable/read', u'Identity')
(u'init', u'NoOp')
(u'save/Const', u'Const')
(u'save/SaveV2/tensor_names', u'Const')
(u'save/SaveV2/shape_and_slices', u'Const')
(u'save/SaveV2', u'SaveV2')
(u'save/control_dependency', u'Identity')
(u'save/RestoreV2/tensor_names', u'Const')
(u'save/RestoreV2/shape_and_slices', u'Const')
(u'save/RestoreV2', u'RestoreV2')
(u'save/Assign', u'Assign')
(u'save/Assign_1', u'Assign')
(u'save/Assign_2', u'Assign')
(u'save/Assign_3', u'Assign')
(u'save/Assign_4', u'Assign')
(u'save/Assign_5', u'Assign')
(u'save/Assign_6', u'Assign')
(u'save/Assign_7', u'Assign')
(u'save/Assign_8', u'Assign')
(u'save/Assign_9', u'Assign')
(u'save/Assign_10', u'Assign')
(u'save/Assign_11', u'Assign')
(u'save/Assign_12', u'Assign')
(u'save/Assign_13', u'Assign')
(u'save/Assign_14', u'Assign')
(u'save/Assign_15', u'Assign')
(u'save/Assign_16', u'Assign')
(u'save/restore_all', u'NoOp')
(u'Reshape/tensor', u'Const')
(u'Reshape/shape', u'Const')
(u'Reshape', u'Reshape')
(u'input_code/initial_value', u'Const')
(u'input_code', u'VariableV2')
(u'input_code/Assign', u'Assign')
(u'input_code/read', u'Identity')
(u'Placeholder_2', u'Placeholder')
(u'ResizeNearestNeighbor/size', u'Const')
(u'ResizeNearestNeighbor', u'ResizeNearestNeighbor')
(u'DVBPR_1/Reshape/shape', u'Const')
(u'DVBPR_1/Reshape', u'Reshape')
(u'DVBPR_1/zeros', u'Const')
(u'DVBPR_1/Conv2D', u'Conv2D')
(u'DVBPR_1/BiasAdd', u'BiasAdd')
(u'DVBPR_1/Relu', u'Relu')
(u'DVBPR_1/Relu_1', u'Relu')
(u'DVBPR_1/MaxPool', u'MaxPool')
(u'DVBPR_1/zeros_1', u'Const')
(u'DVBPR_1/Conv2D_1', u'Conv2D')
(u'DVBPR_1/BiasAdd_1', u'BiasAdd')
(u'DVBPR_1/Relu_2', u'Relu')
(u'DVBPR_1/Relu_3', u'Relu')
(u'DVBPR_1/MaxPool_1', u'MaxPool')
(u'DVBPR_1/zeros_2', u'Const')
(u'DVBPR_1/Conv2D_2', u'Conv2D')
(u'DVBPR_1/BiasAdd_2', u'BiasAdd')
(u'DVBPR_1/Relu_4', u'Relu')
(u'DVBPR_1/Relu_5', u'Relu')
(u'DVBPR_1/zeros_3', u'Const')
(u'DVBPR_1/Conv2D_3', u'Conv2D')
(u'DVBPR_1/BiasAdd_3', u'BiasAdd')
(u'DVBPR_1/Relu_6', u'Relu')
(u'DVBPR_1/Relu_7', u'Relu')
(u'DVBPR_1/zeros_4', u'Const')
(u'DVBPR_1/Conv2D_4', u'Conv2D')
(u'DVBPR_1/BiasAdd_4', u'BiasAdd')
(u'DVBPR_1/Relu_8', u'Relu')
(u'DVBPR_1/Relu_9', u'Relu')
(u'DVBPR_1/MaxPool_2', u'MaxPool')
(u'DVBPR_1/Reshape_1/shape', u'Const')
(u'DVBPR_1/Reshape_1', u'Reshape')
(u'DVBPR_1/MatMul', u'MatMul')
(u'DVBPR_1/zeros_5/shape_as_tensor', u'Const')
(u'DVBPR_1/zeros_5/Const', u'Const')
(u'DVBPR_1/zeros_5', u'Fill')
(u'DVBPR_1/Add', u'Add')
(u'DVBPR_1/Relu_10', u'Relu')
(u'DVBPR_1/dropout/keep_prob', u'Const')
(u'DVBPR_1/MatMul_1', u'MatMul')
(u'DVBPR_1/zeros_6/shape_as_tensor', u'Const')
(u'DVBPR_1/zeros_6/Const', u'Const')
(u'DVBPR_1/zeros_6', u'Fill')
(u'DVBPR_1/Add_1', u'Add')
(u'DVBPR_1/Relu_11', u'Relu')
(u'DVBPR_1/dropout_1/keep_prob', u'Const')
(u'DVBPR_1/MatMul_2', u'MatMul')
(u'DVBPR_1/zeros_7', u'Const')
(u'DVBPR_1/Add_2', u'Add')
(u'Placeholder_3', u'Placeholder')
(u'GatherV2/axis', u'Const')
(u'GatherV2', u'GatherV2')
(u'transpose/Rank', u'Rank')
(u'transpose/sub/y', u'Const')
(u'transpose/sub', u'Sub')
(u'transpose/Range/start', u'Const')
(u'transpose/Range/delta', u'Const')
(u'transpose/Range', u'Range')
(u'transpose/sub_1', u'Sub')
(u'transpose', u'Transpose')
(u'MatMul', u'MatMul')
(u'Sum/reduction_indices', u'Const')
(u'Sum', u'Sum')

Code:

# get all placeholders in graph

placeholders = [ op for op in sess.graph.get_operations() if op.type == "Placeholder"]

placeholders

Output:

[<tf.Operation 'Placeholder' type=Placeholder>,
 <tf.Operation 'Placeholder_1' type=Placeholder>,
 <tf.Operation 'Placeholder_2' type=Placeholder>,
 <tf.Operation 'Placeholder_3' type=Placeholder>]

Update 2:

from main.py in DVBPR folder at https://github.com/kang205/DVBPR

#define model
with tf.device('/gpu:0'):
    #training sample
    queueu = tf.placeholder(dtype=tf.int32,shape=[1])
    queuei = tf.placeholder(dtype=tf.int32,shape=[1])
    queuej = tf.placeholder(dtype=tf.int32,shape=[1])
    queueimage1 = tf.placeholder(dtype=tf.uint8,shape=[224,224,3])
    queueimage2 = tf.placeholder(dtype=tf.uint8,shape=[224,224,3])
    batch_train_queue = tf.FIFOQueue(batch_size*5, dtypes=[tf.int32,tf.int32,tf.int32,tf.uint8,tf.uint8], shapes=[[1],[1],[1],[224,224,3],[224,224,3]])
    batch_train_queue_op = batch_train_queue.enqueue([queueu,queuei,queuej,queueimage1,queueimage2]);
    u,i,j,image1,image2 = batch_train_queue.dequeue_many(batch_size)

    image_test=tf.placeholder(dtype=tf.uint8,shape=[batch_size,224,224,3])

Answer 1

Good questions. First, feed_dict is simply a python dictionary in which each key is a tf.placeholder and each corresponding value is a python object. This object must have a shape equal to that of the corresponding placeholder, and must have a datatype which can be coerced into the placeholders dtype. The structure of feed_dict is dictated by the structure of the graph, because there must be one dictionary key-value tuple for each placeholder in the graph.

To get all of the placeholders in the graph, the following one-liner will do:

placeholders = [ op for op in graph.get_operations() if op.type == "Placeholder"]

Credit for that solution goes to this comment on a related TensorFlow issue. This one-liner works by reviewing each operation and appending it to placeholders if the operation type is "Placeholder".

Answer 2

feed_dict is just a dictionary where the key is the variable containing tensor information, and the value is the data to be fed to the network. Usually you can populate the session graph and find the placeholders, as they can only be inputs to a graph. You can populate the graph by:

for op in sess.graph.get_operations(): print(op.name, op.type)

As each session can depend on a different graph, feed_dict can have different inputs. If you are defining your own graph, it is good practice to keep the input placeholders as different variables.

Also your sess.run command is extracting inputs from the session.