def main():
epoch_number = 30
learning_rate = 0.01
train_features = [1.0, 2.0, 3.0, 4.0, 5.0]
train_labels = [10.0, 20.0, 30.0, 40.0, 50.0]
weights = ops.VariableOp(0.0)
bias = ops.VariableOp(0.0)
x = ops.PlaceholderOp(float)
y = ops.PlaceholderOp(float)
predict = weights * x + bias
loss = (y - predict)**2
sgd_optimizer = optimizer.GradientDescentOptimizer(learning_rate)
train_op = sgd_optimizer.minimize(loss)
with session.Session() as sess:
for epoch_index in range(epoch_number):
# Take one sample from train dataset
sample_number = len(train_features)
train_feature = train_features[epoch_index % sample_number]
train_label = train_labels[epoch_index % sample_number]
# Update model variables and print loss
sess.run(train_op, feed_dict={x: train_feature, y: train_label})
loss_value = sess.run(loss, feed_dict={x: 1.0, y: 10.0})
print("Epoch: {}, loss: {}, weight: {}, bias: {}".format(
epoch_index, loss_value, weights.get_value(),
bias.get_value()))
def main1():
# y = 3 * x**3 + 2 * x**2 + x + 10
# y' = 9 * x**2 + 4 * x + 1
print("Formula: {}".format("y = 3 * x**3 + 2 * x**2 + x + 10"))
print("Formula gradient: {}".format("y' = 9 * x**2 + 4 * x + 1"))
x = 1
first_item = ops.MultipleOp(3, ops.CubicOp(x))
second_item = ops.MultipleOp(2, ops.SquareOp(x))
third_item = ops.VariableOp(x)
forth_item = ops.ConstantOp(10)
y = ops.AddNOp(ops.AddOp(first_item, second_item), third_item, forth_item)
#import ipdb;ipdb.set_trace()
# Should be "X: 1, forward: 16.0, grad: 14.0"
print("X: {}, forward: {}, grad: {}".format(x, y.forward(), y.grad()))
# Build the graph
name_op_map = {}
coefficient1 = ops.ConstantOp(3)
cubic1 = ops.CubicOp(x)
coefficient2 = ops.ConstantOp(2)
square1 = ops.SquareOp(x)
multiple1 = ops.MultipleOp(coefficient1, cubic1)
multiple2 = ops.MultipleOp(coefficient2, square1)
variable1 = ops.VariableOp(x)
constant1 = ops.ConstantOp(10)
add1 = ops.AddNOp(multiple1, multiple2, variable1, constant1)
add2 = ops.AddNOp(multiple1, multiple2)
name_op_map["coefficient1"] = coefficient1
name_op_map["cubic1"] = cubic1
name_op_map["coefficient2"] = coefficient2
name_op_map["square1"] = square1
name_op_map["multiple1"] = multiple1
name_op_map["multiple2"] = multiple2
name_op_map["variable1"] = variable1
name_op_map["constant1"] = constant1
name_op_map["add1"] = add1
name_op_map["add2"] = add2
print("add1 forward: {}, backward: {}".format(
name_op_map["add1"].forward(), name_op_map["add1"].grad()))
print("add2 forward: {}, backward: {}".format(
name_op_map["add2"].forward(), name_op_map["add2"].grad()))
'''
# Automatically update weights with gradient
learning_rate = 0.01
epoch_number = 10
label = 10
weights_value = 3
# y = weights * x**3 + 2 * x**2 + x + 10
x = ops.PlaceholderOp()
weights = ops.VariableOp(weights_value)
cubic1 = ops.CubicOp(x)
coefficient2 = ops.ConstantOp(2)
square1 = ops.SquareOp(x)
multiple1 = ops.MultipleOp(weights, cubic1)
multiple2 = ops.MultipleOp(coefficient2, square1)
constant1 = ops.ConstantOp(10)
add1 = ops.AddNOp(multiple1, multiple2, x, constant1)
for epoch_index in range(epoch_number):
x.set_value(1)
grad = add1.grad()
weights_value -= learning_rate * grad
weights.set_value(weights_value)
predict = add1.forward()
loss = predict - label
print("Epoch: {}, loss: {}, grad: {}, weights: {}, predict: {}".format(
epoch_index, loss, grad, weights_value, predict))
'''
# Run with session
hello = ops.ConstantOp("Hello, TensorFlow! -- by MinialFlow")
sess = session.Session()
print(sess.run(hello))
a = ops.ConstantOp(10)
b = ops.ConstantOp(32)
c = ops.AddOp(a, b)
print(sess.run(c))
# Run with session and feed_dict
a = ops.PlaceholderOp()
b = ops.ConstantOp(32)
c = a + b
sess = session.Session()
print(sess.run(c, feed_dict={a: 10}))
print(sess.run(c, feed_dict={a.name: 10}))
# Run with python operators
a = ops.ConstantOp(32.0)
b = ops.ConstantOp(10.0)
print(sess.run(a + b))
print(sess.run(a - b))
print(sess.run(a * b))
print(sess.run(a / b))
# Operate with primitive variables
a = ops.ConstantOp(32.0)
b = 10.0
print(sess.run(a + b))
print(sess.run(a - b))
print(sess.run(a * b))
print(sess.run(a / b))
# Operate by primitive variables
a = 32.0
b = ops.ConstantOp(10.0)
print(sess.run(a + b))
print(sess.run(a - b))
print(sess.run(a * b))
print(sess.run(a / b))