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from __future__ import print_function | |
import tensorflow as tf | |
a = tf.placeholder(tf.int32) | |
b = tf.placeholder(tf.int32) | |
# Define some operations | |
add = tf.add(a, b) | |
sub = tf.subtract(a, b) |
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from __future__ import print_function | |
import tensorflow as tf | |
# Create a Constant op that produces a 1x2 matrix. The op is | |
# added as a node to the default graph. | |
matrix1 = tf.constant([[3., 3.]]) | |
# Create another Constant that produces a 2x1 matrix. |
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import tensorflow as tf; | |
print(tf.__version__) |
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from __future__ import print_function | |
import tensorflow as tf | |
import numpy | |
import matplotlib.pyplot as plt |
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rng = numpy.random |
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learning_rate = 0.01 | |
training_epochs = 1000 | |
display_step = 50 |
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train_X = numpy.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167, | |
7.042,10.791,5.313,7.997,5.654,9.27,3.1]) | |
train_Y = numpy.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221, | |
2.827,3.465,1.65,2.904,2.42,2.94,1.3]) |
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n_samples = train_X.shape[0] |
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# tf Graph Input | |
X = tf.placeholder("float") | |
Y = tf.placeholder("float") |
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# Set model weights | |
W = tf.Variable(rng.randn(), name="weight") | |
b = tf.Variable(rng.randn(), name="bias") |