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• 我的博客：TensorFlow 核心——数据流图

TensorFlow 是基于数据流图 (Data Flow Graph), 支持自动微分 (简称AD) 的数值计算库。本文仅仅考虑低级 API.

TensorFlow 的计算图模型一般分为两个步骤：创建计算图，在 Session 中运行。(暂不考虑 Eager)

为了更好的管理模型，最好在特定的 Graph 中创建模型，且对于实现不同功能的模块最好按照 name_scope 对其进行划分。下面是一个 Demo：

# Explicitly create a Graph object
graph = tf.Graph()

with graph.as_default():
    
    with tf.name_scope("variables"):
        # Variable to keep track of how many times the graph has been run
        global_step = tf.Variable(0, dtype=tf.int32, name="global_step")
        
        # Variable that keeps track of the sum of all output values over time:
        total_output = tf.Variable(0.0, dtype=tf.float32, name="total_output")
    
    # Primary transformation Operations
    with tf.name_scope("transformation"):
        
        # Separate input layer
        with tf.name_scope("input"):
            # Create input placeholder- takes in a Vector 
            a = tf.placeholder(tf.float32, shape=[None], name="input_placeholder_a")
    
        # Separate middle layer
        with tf.name_scope("intermediate_layer"):
            b = tf.reduce_prod(a, name="product_b")
            c = tf.reduce_sum(a, name="sum_c")
        
        # Separate output layer
        with tf.name_scope("output"):
            output = tf.add(b, c, name="output")
        
    with tf.name_scope("update"):
        # Increments the total_output Variable by the latest output
        update_total = total_output.assign_add(output)
        
        # Increments the above `global_step` Variable, should be run whenever the graph is run
        increment_step = global_step.assign_add(1)
    
    # Summary Operations
    with tf.name_scope("summaries"):
        avg = tf.div(update_total, tf.cast(increment_step, tf.float32), name="average")
        
        # Creates summaries for output node
        tf.summary.scalar('Output', output)
        tf.summary.scalar('Sum of outputs over time', update_total)
        tf.summary.scalar('Average of outputs over time', avg)
    
    # Global Variables and Operations
    with tf.name_scope("global_ops"):
        # Initialization Op
        init = tf.initialize_all_variables()    
        # Merge all summaries into one Operation
        merged_summaries = tf.summary.merge_all()

def run_graph(input_tensor):
    """
    Helper function; runs the graph with given input tensor and saves summaries
    """
    feed_dict = {a: input_tensor}
    out, step, summary = sess.run([output, increment_step, merged_summaries], feed_dict=feed_dict)
    writer.add_summary(summary, global_step=step)

# Start a Session, using the explicitly created Graph
sess = tf.Session(graph=graph)

# Open a SummaryWriter to save summaries
writer = tf.summary.FileWriter('../graph/improved_graph', graph)

# Initialize Variables
sess.run(init)
# Run the graph with various inputs
run_graph([2,8])
run_graph([3,1,3,3])
run_graph([8])
run_graph([1,2,3])
run_graph([11,4])
run_graph([4,1])
run_graph([7,3,1])
run_graph([6,3])
run_graph([0,2])
run_graph([4,5,6])
# Write the summaries to disk
writer.flush()

# Close the SummaryWriter
writer.close()

# Close the session
sess.close()