def test_matmul():
x = tf.placeholder(tf.float32)
y = tf.placeholder(tf.float32)
ax = np.ones((2, 3))
ay = np.ones((3, 4)) * 4
z = tf.matmul(x, y) * 4
sess = tf.Session()
az = sess.run(z, feed_dict={x: ax, y: ay})
np.testing.assert_almost_equal(az, np.dot(ax, ay) * 4)
def test_matmul():
x = tf.placeholder(tf.float32)
y = tf.placeholder(tf.float32)
ax = np.ones((2, 3))
ay = np.ones((3, 4)) * 4
z = tf.matmul(x, y) * 4
sess = tf.Session()
az = sess.run(z, feed_dict={x:ax, y:ay})
np.testing.assert_almost_equal(
az, np.dot(ax, ay) * 4)
def test_matmul_grad():
x = tf.placeholder(tf.float32)
y = tf.placeholder(tf.float32)
ax = np.ones((2, 3))
ay = np.ones((3, 4)) * 4
z = tf.matmul(x, y) * 4
gx, gy = tf.gradients(z, [x, y])
sess = tf.Session()
agx = sess.run(gx, feed_dict={x: ax, y: ay})
agy = sess.run(gy, feed_dict={x: ax, y: ay})
np.testing.assert_almost_equal(agx, np.dot(np.ones((2, 4)), ay.T) * 4)
np.testing.assert_almost_equal(agy, np.dot(ax.T, np.ones((2, 4))) * 4)
def test_matmul_grad():
x = tf.placeholder(tf.float32)
y = tf.placeholder(tf.float32)
ax = np.ones((2, 3))
ay = np.ones((3, 4)) * 4
z = tf.matmul(x, y) * 4
gx, gy = tf.gradients(z, [x, y])
sess = tf.Session()
agx = sess.run(gx, feed_dict={x:ax, y:ay})
agy = sess.run(gy, feed_dict={x:ax, y:ay})
np.testing.assert_almost_equal(
agx,
np.dot(np.ones((2,4)), ay.T) * 4)
np.testing.assert_almost_equal(
agy,
np.dot(ax.T, np.ones((2,4))) * 4)
"""Tinyflow example code.
Minimum softmax code that exposes the optimizer.
"""
import tinyflow as tf
from tinyflow.datasets import get_mnist
# Create the model
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
y = tf.nn.softmax(tf.matmul(x, W))
# Define loss and optimizer
y_ = tf.placeholder(tf.float32, [None, 10])
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
learning_rate = 0.5
W_grad = tf.gradients(cross_entropy, [W])[0]
train_step = tf.assign(W, W - learning_rate * W_grad)
sess = tf.Session()
sess.run(tf.initialize_all_variables())
# get the mnist dataset
mnist = get_mnist(flatten=True, onehot=True)
for i in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={x: batch_xs, y_:batch_ys})
"""Tinyflow example code.
This code is adapted from Tensorflow's MNIST Tutorial with minimum code changes.
"""
import tinyflow as tf
from tinyflow.datasets import get_mnist
# Create the model
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
y = tf.nn.softmax(tf.matmul(x, W))
# Define loss and optimizer
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)
sess = tf.Session()
sess.run(tf.initialize_all_variables())
# get the mnist dataset
mnist = get_mnist(flatten=True, onehot=True)
print("minist download is completed!")
for i in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})