2016-12-06

128

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## THE DATA FLOW GRAPH

The data flow graph below can vividly demonstrate why the Google Open Source Software Library for Machine Intelligence is called TensorFlow (TF). I think the graph is impressive enough to give every begginer an intuitive understanding of TF. ## HELLO, TENSORFLOW

TF was open-sourced at github and fully documented. It's very easy to get started if you refer to the tutorials. Here we use TF to fit a random generated hyper plane with TF's python API.

``````import tensorflow as tf
import numpy as np

# Dimension
dim = 3

# Weights and bias
weights = np.random.random([dim,dim])
bias = np.random.random([dim])

# Show original weights and bias
print("Weights:")
print(weights)
print("Bias:")
print(bias)

# Create 100 phony x, y data points in NumPy, y = x.*w + b
x_data = np.random.rand(100, dim).astype(np.float32)
y_data = np.dot(x_data, weights) + bias

# Try to find values for W and b that compute y_data = x_data.*W + b
W = tf.Variable(tf.random_uniform([dim, dim], 0.0, 1.0))
b = tf.Variable(tf.zeros([dim]))
y = tf.matmul(x_data, W) + b

# Minimize the mean squared errors.
loss = tf.reduce_mean(tf.square(y - y_data))
train = optimizer.minimize(loss)

# Before starting, initialize the variables.  We will 'run' this first.
init = tf.initialize_all_variables()

# Launch the graph.
sess = tf.Session()
sess.run(init)

# Fit the original parameters.
for step in range(2001):
sess.run(train)
if step % 200 == 0:
print(step, sess.run(W), sess.run(b))

``````

Let's RUN these codes to see what we will get. Do as below.

-> New a python script named "fitter.py" in any of your workspace folders.

-> Open "fitter.py" with your favorite text editor, let's say gedit.

-> Copy and paste these codes to "fitter.py" in gedit.

-> Open your command line shell in where "fitter.py" is.

-> Type the script below in the opened command line shell window, then press Enter.

``python fitter.py``

If you see the screen roll and display something like below, congratulations, you've make the first acquaintance with TensorFlow. ### 全部评论：0条 ### 热评文章   ### 推荐文章 2016-09-13 22:21:25

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