def main(_):
mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
# GOLANG note that we must label the input-tensor!
x = tf.placeholder(tf.float32, [None, 784], name="imageinput")
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.add(tf.matmul(x, W) , b)
y_ = tf.placeholder(tf.float32, [None, 10])
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
sess = tf.InteractiveSession()
tf.global_variables_initializer().run()
# Train
for _ in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
# GOLANG note that we must label the infer-operation!!
infer = tf.argmax(y,1, name="infer")
correct_prediction = tf.equal(infer, tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(sess.run(accuracy, feed_dict={x: mnist.test.images,
y_: mnist.test.labels}))
builder = tf.saved_model.builder.SavedModelBuilder("mnistmodel")
# GOLANG note that we must tag our model so that we can retrieve it at inference-time
builder.add_meta_graph_and_variables(sess,["serve"])
builder.save()
python类InteractiveSession()的实例源码
code-05-RunGraphWithError.py 文件源码
项目：handson-tensorflow
作者: winnietsang
项目源码
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def main():
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
# Placeholder that will be fed image data.
x = tf.placeholder(tf.float32, [None, 784])
# Placeholder that will be fed the correct labels.
y_ = tf.placeholder(tf.float32, [None, 10])
# Define weight and bias.
W = weight_variable([784, 10])
b = bias_variable([10])
# Here we define our model which utilizes the softmax regression.
y = tf.nn.softmax(tf.matmul(x, W) + b)
# Define our loss.
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
# Define our optimizer.
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
# Define accuracy.
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
correct_prediction = tf.cast(correct_prediction, tf.float32)
accuracy = tf.reduce_mean(correct_prediction)
# Launch session.
sess = tf.InteractiveSession()
# Do the training.
for i in range(1100):
batch = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={x: batch[0], y_: batch[1]})
# See how model did.
print("Test Accuracy %g" % sess.run(accuracy, feed_dict={x: mnist.test.images,
y_: mnist.test.labels}))
def main(_):
# Import data
mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
# Create the model
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.matmul(x, W) + b
# Define loss and optimizer
y_ = tf.placeholder(tf.float32, [None, 10])
# The raw formulation of cross-entropy,
#
# tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.nn.softmax(y)),
# reduction_indices=[1]))
#
# can be numerically unstable.
#
# So here we use tf.nn.softmax_cross_entropy_with_logits on the raw
# outputs of 'y', and then average across the batch.
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
sess = tf.InteractiveSession()
tf.global_variables_initializer().run()
# Train
for _ in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
# Test trained model
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(sess.run(accuracy, feed_dict={x: mnist.test.images,
y_: mnist.test.labels}))
def main():
digits = load_digits()
x_train, x_test, y_train_, y_test_ = cross_validation.train_test_split(digits.data, digits.target, test_size=0.2,
random_state=0)
lb = preprocessing.LabelBinarizer()
lb.fit(digits.target)
y_train = lb.transform(y_train_)
y_test = lb.transform(y_test_)
sess = tf.InteractiveSession()
x = tf.placeholder(tf.float32, shape=[None, 64])
y_ = tf.placeholder(tf.float32, shape=[None, 10])
w_1 = weight_variable([64, 32])
b_1 = bias_variable([32])
h_1 = tf.nn.relu(tf.matmul(x, w_1) + b_1)
w_2 = weight_variable([32, 10])
b_2 = bias_variable([10])
y = tf.nn.softmax(tf.matmul(h_1, w_2) + b_2)
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
sess.run(tf.initialize_all_variables())
for i in range(1000):
train_step.run(feed_dict={x: x_train, y_: y_train})
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(accuracy.eval(feed_dict={x: x_test, y_: y_test}))
def main():
digits = load_digits()
x_train, x_test, y_train_, y_test_ = cross_validation.train_test_split(digits.data, digits.target, test_size=0.2,
random_state=0)
lb = preprocessing.LabelBinarizer()
lb.fit(digits.target)
y_train = lb.transform(y_train_)
y_test = lb.transform(y_test_)
sess = tf.InteractiveSession()
x = tf.placeholder(tf.float32, shape=[None, 64])
y_ = tf.placeholder(tf.float32, shape=[None, 10])
phase_train = tf.placeholder(tf.bool, name='phase_train')
w_1 = weight_variable([64, 32])
b_1 = bias_variable([32])
t_1 = tf.matmul(x, w_1) + b_1
bn = batch_norm(t_1, 1, phase_train)
h_1 = binarized_ops.binarized(bn)
w_2 = weight_variable([32, 10])
b_2 = bias_variable([10])
y = tf.nn.softmax(tf.matmul(h_1, w_2) + b_2)
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
sess.run(tf.initialize_all_variables())
for i in range(1000):
train_step.run(feed_dict={x: x_train, y_: y_train, phase_train: True})
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(accuracy.eval(feed_dict={x: x_test, y_: y_test, phase_train: False}))
def testOverwriteOutput():
sess = tf.InteractiveSession()
external_input = [0, 1., 0., 1., 1.]
graph_input = [-5.5, 4.4, 3.4, -2.3, 1.9]
result = overwrite_output(graph_input, external_input)
with sess.as_default():
print(result.eval())
def testBinarized():
sess = tf.InteractiveSession()
result = binarized([-5.5, 4.4, 3.4, -2.3, 1.9])
with sess.as_default():
print(result.eval())
def main():
digits = load_digits()
x_train, x_test, y_train_, y_test_ = cross_validation.train_test_split(digits.data, digits.target, test_size=0.2,
random_state=0)
lb = preprocessing.LabelBinarizer()
lb.fit(digits.target)
y_train = lb.transform(y_train_)
y_test = lb.transform(y_test_)
sess = tf.InteractiveSession()
x = tf.placeholder(tf.float32, shape=[None, 64])
y_ = tf.placeholder(tf.float32, shape=[None, 10])
w_1 = weight_variable([64, 32])
b_1 = bias_variable([32])
h_1 = tf.nn.relu(tf.matmul(x, w_1) + b_1)
w_2 = weight_variable([32, 10])
b_2 = bias_variable([10])
y = tf.nn.softmax(tf.matmul(h_1, w_2) + b_2)
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
sess.run(tf.initialize_all_variables())
for i in range(1000):
train_step.run(feed_dict={x: x_train, y_: y_train})
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(accuracy.eval(feed_dict={x: x_test, y_: y_test}))
def testOverwriteOutput():
sess = tf.InteractiveSession()
external_input = [0, 1., 0., 1., 1.]
graph_input = [-5.5, 4.4, 3.4, -2.3, 1.9]
result = overwrite_output(graph_input, external_input)
with sess.as_default():
print(result.eval())
def testBinarized():
sess = tf.InteractiveSession()
result = binarized([-5.5, 4.4, 3.4, -2.3, 1.9])
with sess.as_default():
print(result.eval())
def main():
digits = load_digits()
x_train, x_test, y_train_, y_test_ = cross_validation.train_test_split(digits.data, digits.target, test_size=0.2,
random_state=0)
lb = preprocessing.LabelBinarizer()
lb.fit(digits.target)
y_train = lb.transform(y_train_)
y_test = lb.transform(y_test_)
sess = tf.InteractiveSession()
x = tf.placeholder(tf.float32, shape=[None, 64])
y_ = tf.placeholder(tf.float32, shape=[None, 10])
w_1 = weight_variable([64, 32])
b_1 = bias_variable([32])
h_1 = tf.nn.relu(tf.matmul(x, w_1) + b_1)
w_2 = weight_variable([32, 10])
b_2 = bias_variable([10])
y = tf.nn.softmax(tf.matmul(h_1, w_2) + b_2)
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
sess.run(tf.initialize_all_variables())
for i in range(1000):
train_step.run(feed_dict={x: x_train, y_: y_train})
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(accuracy.eval(feed_dict={x: x_test, y_: y_test}))
def main():
digits = load_digits()
x_train, x_test, y_train_, y_test_ = cross_validation.train_test_split(digits.data, digits.target, test_size=0.2,
random_state=0)
lb = preprocessing.LabelBinarizer()
lb.fit(digits.target)
y_train = lb.transform(y_train_)
y_test = lb.transform(y_test_)
sess = tf.InteractiveSession()
x = tf.placeholder(tf.float32, shape=[None, 64])
y_ = tf.placeholder(tf.float32, shape=[None, 10])
phase_train = tf.placeholder(tf.bool, name='phase_train')
w_1 = weight_variable([64, 32])
b_1 = bias_variable([32])
t_1 = tf.matmul(x, w_1) + b_1
bn = batch_norm(t_1, 1, phase_train)
h_1 = binarized_ops.binarized(bn)
w_2 = weight_variable([32, 10])
b_2 = bias_variable([10])
y = tf.nn.softmax(tf.matmul(h_1, w_2) + b_2)
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
sess.run(tf.initialize_all_variables())
for i in range(1000):
train_step.run(feed_dict={x: x_train, y_: y_train, phase_train: True})
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(accuracy.eval(feed_dict={x: x_test, y_: y_test, phase_train: False}))
def testOverwriteOutput():
sess = tf.InteractiveSession()
external_input = [0, 1., 0., 1., 1.]
graph_input = [-5.5, 4.4, 3.4, -2.3, 1.9]
result = overwrite_output(graph_input, external_input)
with sess.as_default():
print(result.eval())
def main():
digits = load_digits()
x_train, x_test, y_train_, y_test_ = cross_validation.train_test_split(digits.data, digits.target, test_size=0.2,
random_state=0)
lb = preprocessing.LabelBinarizer()
lb.fit(digits.target)
y_train = lb.transform(y_train_)
y_test = lb.transform(y_test_)
sess = tf.InteractiveSession()
x = tf.placeholder(tf.float32, shape=[None, 64])
y_ = tf.placeholder(tf.float32, shape=[None, 10])
w_1 = weight_variable([64, 32])
b_1 = bias_variable([32])
h_1 = tf.nn.relu(tf.matmul(x, w_1) + b_1)
w_2 = weight_variable([32, 10])
b_2 = bias_variable([10])
y = tf.nn.softmax(tf.matmul(h_1, w_2) + b_2)
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
sess.run(tf.initialize_all_variables())
for i in range(1000):
train_step.run(feed_dict={x: x_train, y_: y_train})
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(accuracy.eval(feed_dict={x: x_test, y_: y_test}))
def main():
digits = load_digits()
x_train, x_test, y_train_, y_test_ = cross_validation.train_test_split(digits.data, digits.target, test_size=0.2,
random_state=0)
lb = preprocessing.LabelBinarizer()
lb.fit(digits.target)
y_train = lb.transform(y_train_)
y_test = lb.transform(y_test_)
sess = tf.InteractiveSession()
x = tf.placeholder(tf.float32, shape=[None, 64])
y_ = tf.placeholder(tf.float32, shape=[None, 10])
phase_train = tf.placeholder(tf.bool, name='phase_train')
w_1 = weight_variable([64, 32])
b_1 = bias_variable([32])
t_1 = tf.matmul(x, w_1) + b_1
bn = batch_norm(t_1, 1, phase_train)
h_1 = binarized_ops.binarized(bn)
w_2 = weight_variable([32, 10])
b_2 = bias_variable([10])
y = tf.nn.softmax(tf.matmul(h_1, w_2) + b_2)
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
sess.run(tf.initialize_all_variables())
for i in range(1000):
train_step.run(feed_dict={x: x_train, y_: y_train, phase_train: True})
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(accuracy.eval(feed_dict={x: x_test, y_: y_test, phase_train: False}))
def testOverwriteOutput():
sess = tf.InteractiveSession()
external_input = [0, 1., 0., 1., 1.]
graph_input = [-5.5, 4.4, 3.4, -2.3, 1.9]
result = overwrite_output(graph_input, external_input)
with sess.as_default():
print(result.eval())
def testBinarized():
sess = tf.InteractiveSession()
result = binarized([-5.5, 4.4, 3.4, -2.3, 1.9])
with sess.as_default():
print(result.eval())
def main():
digits = load_digits()
x_train, x_test, y_train_, y_test_ = cross_validation.train_test_split(digits.data, digits.target, test_size=0.2,
random_state=0)
lb = preprocessing.LabelBinarizer()
lb.fit(digits.target)
y_train = lb.transform(y_train_)
y_test = lb.transform(y_test_)
sess = tf.InteractiveSession()
x = tf.placeholder(tf.float32, shape=[None, 64])
y_ = tf.placeholder(tf.float32, shape=[None, 10])
w_1 = weight_variable([64, 32])
b_1 = bias_variable([32])
h_1 = tf.nn.relu(tf.matmul(x, w_1) + b_1)
w_2 = weight_variable([32, 10])
b_2 = bias_variable([10])
y = tf.nn.softmax(tf.matmul(h_1, w_2) + b_2)
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
sess.run(tf.initialize_all_variables())
for i in range(1000):
train_step.run(feed_dict={x: x_train, y_: y_train})
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(accuracy.eval(feed_dict={x: x_test, y_: y_test}))
def testOverwriteOutput():
sess = tf.InteractiveSession()
external_input = [0, 1., 0., 1., 1.]
graph_input = [-5.5, 4.4, 3.4, -2.3, 1.9]
result = overwrite_output(graph_input, external_input)
with sess.as_default():
print(result.eval())
def testBinarized():
sess = tf.InteractiveSession()
result = binarized([-5.5, 4.4, 3.4, -2.3, 1.9])
with sess.as_default():
print(result.eval())
