# -*- coding: utf-8 -*-

import tensorflow as tf
w1=tf.Variable(tf.random_normal([2,6],stddev=1))
w2=tf.Variable(tf.random_normal([6,1],stddev=1))

x=tf.placeholder(dtype=tf.float32,shape=(4,2),name="input")
h=tf.matmul(x,w1)
y=tf.matmul(h,w2)

init_op=tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init_op)

    print sess.run(y,feed_dict={x:[[5.2,2.9],[3.9,1.1],[3.9,5.2],[6.1,9.2]]})
 

資料需要通過字典輸入

Launch the graph in a session.

with tf.Session() as sess:
    # Run the variable initializer.
    sess.run(w.initializer)
    # ...you now can run ops that use the value of 'w'...

#
global_variables_initializer()
to add an Op to the graph that initializes all the variables. You then run that Op after launching the graph.Add an Op to initialize global variables.
init_op = tf.global_variables_initializer()

Launch the graph in a session.

with tf.Session() as sess:
    # Run the Op that initializes global variables.
    sess.run(init_op)
    # ...you can now run any Op that uses variable values...
tf.Variable

_init__(
    initial_value=None,
    trainable=True,
    collections=None,
    validate_shape=True,
    caching_device=None,
    name=None,
    variable_def=None,
    dtype=None,
    expected_shape=None,
    import_scope=None
)

Creates a new variable with value initial_value.

The new variable is added to the graph collections listed in collections, which defaults to [GraphKeys.GLOBAL_VARIABLES].

If trainable is True the variable is also added to the graph collection GraphKeys.TRAINABLE_VARIABLES.

This constructor creates both a variable Op and an assign Op to set the variable to its initial value.

Args:

initial_value: A Tensor, or Python object convertible to a Tensor, which is the initial value for the Variable. The initial value must have a shape specified unless validate_shape is set to False. Can also be a callable with no argument that returns the initial value when called. In that case, dtype must be specified. (Note that initializer functions from init_ops.py must first be bound to a shape before being used here.)
trainable: If True, the default, also adds the variable to the graph collection GraphKeys.TRAINABLE_VARIABLES. This collection is used as the default list of variables to use by the Optimizer classes.
collections: List of graph collections keys. The new variable is added to these collections. Defaults to [GraphKeys.GLOBAL_VARIABLES].
validate_shape: If False, allows the variable to be initialized with a value of unknown shape. If True, the default, the shape of initial_value must be known.
caching_device: Optional device string describing where the Variable should be cached for reading. Defaults to the Variable's device. If not None, caches on another device. Typical use is to cache on the device where the Ops using the Variable reside, to deduplicate copying through Switch and other conditional statements.
name: Optional name for the variable. Defaults to 'Variable' and gets uniquified automatically.
variable_def: VariableDef protocol buffer. If not None, recreates the Variable object with its contents, referencing the variable's nodes in the graph, which must already exist. The graph is not changed.variable_def and the other arguments are mutually exclusive.
dtype: If set, initial_value will be converted to the given type. If None, either the datatype will be kept (if initial_value is a Tensor), or convert_to_tensor will decide.
expected_shape: A TensorShape. If set, initial_value is expected to have this shape.
import_scope: Optional string. Name scope to add to the Variable. Only used when initializing from protocol buffer.
Raises:

ValueError: If both variable_def and initial_value are specified.
ValueError: If the initial value is not specified, or does not have a shape and validate_shape is True.