tfgraphviz

In [1]:

    

import tensorflow as tf
import tfgraphviz as tfg


    

In [2]:

    

calc_g = tf.Graph()


    

In [3]:

    

with calc_g.as_default():
    a = tf.constant(1, name="a")
    b = tf.constant(2, name="b")
    c = tf.add(a, b, name="add")


    

In [4]:

    

tfg.board(calc_g)


    

    
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In [5]:

    

reg_g = tf.Graph()


    

In [6]:

    

with reg_g.as_default():
    import numpy as np
    x_data = np.random.rand(100).astype(np.float32)
    y_data = x_data * 0.1 + 0.3
    W = tf.Variable(tf.random_uniform([1], -1.0, 1.0))
    b = tf.Variable(tf.zeros([1]))
    y = W * x_data + b
    loss = tf.reduce_mean(tf.square(y - y_data))
    optimizer = tf.train.GradientDescentOptimizer(0.5)
    train = optimizer.minimize(loss)


    

In [7]:

    

tfg.board(reg_g)


    

    
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In [8]:

    

tfg.board(reg_g, depth=2)


    

    
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In [9]:

    

mnist_g = tf.Graph()


    

In [10]:

    

with mnist_g.as_default():
    x = tf.placeholder(tf.float32, [None, 784])
    W = tf.Variable(tf.zeros([784, 10]))
    b = tf.Variable(tf.zeros([10]))
    y = tf.nn.softmax(tf.matmul(x, W) + b)
    y_ = tf.placeholder(tf.float32, [None, 10])
    cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
    train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)


    

In [11]:

    

tfg.board(mnist_g, depth=1)


    

    
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