def test_recognize_digits_conv(self):
program = Program()
with program_guard(program, startup_program=Program()):
images = layers.data(
name='pixel', shape=[1, 28, 28], dtype='float32')
label = layers.data(name='label', shape=[1], dtype='int32')
conv_pool_1 = nets.simple_img_conv_pool(
input=images,
filter_size=5,
num_filters=2,
pool_size=2,
pool_stride=2,
act="relu")
conv_pool_2 = nets.simple_img_conv_pool(
input=conv_pool_1,
filter_size=5,
num_filters=4,
pool_size=2,
pool_stride=2,
act="relu")
predict = layers.fc(input=conv_pool_2, size=10, act="softmax")
cost = layers.cross_entropy(input=predict, label=label)
avg_cost = layers.mean(x=cost)
program.append_backward(avg_cost)
print(str(program))
def test_recognize_digits_conv(self):
program = Program()
with program_guard(program, startup_program=Program()):
images = layers.data(name='pixel',
shape=[1, 28, 28],
dtype='float32')
label = layers.data(name='label', shape=[1], dtype='int32')
conv_pool_1 = nets.simple_img_conv_pool(input=images,
filter_size=5,
num_filters=2,
pool_size=2,
pool_stride=2,
act="relu")
conv_pool_2 = nets.simple_img_conv_pool(input=conv_pool_1,
filter_size=5,
num_filters=4,
pool_size=2,
pool_stride=2,
act="relu")
predict = layers.fc(input=conv_pool_2, size=10, act="softmax")
cost = layers.cross_entropy(input=predict, label=label)
avg_cost = layers.mean(x=cost)
program.append_backward(avg_cost)
print(str(program))
def test_fit_a_line(self):
program = Program()
with program_guard(program, startup_program=Program()):
x = layers.data(name='x', shape=[13], dtype='float32')
y_predict = layers.fc(input=x, size=1, act=None)
y = layers.data(name='y', shape=[1], dtype='float32')
cost = layers.square_error_cost(input=y_predict, label=y)
avg_cost = layers.mean(x=cost)
self.assertIsNotNone(avg_cost)
program.append_backward(avg_cost)
print(str(program))
def test_fit_a_line(self):
program = Program()
with program_guard(program, startup_program=Program()):
x = layers.data(name='x', shape=[13], dtype='float32')
y_predict = layers.fc(input=x, size=1, act=None)
y = layers.data(name='y', shape=[1], dtype='float32')
cost = layers.square_error_cost(input=y_predict, label=y)
avg_cost = layers.mean(x=cost)
self.assertIsNotNone(avg_cost)
program.append_backward(avg_cost)
print(str(program))
def test_append_backward(self):
prog = Program()
block = prog.global_block()
mul_x = block.create_var(
dtype="float32", shape=[5, 10], lod_level=0, name="mul.x")
mul_y = block.create_var(
dtype="float32", shape=[10, 8], lod_level=0, name="mul.y")
mul_out = block.create_var(
dtype="float32", shape=[5, 8], lod_level=0, name="mul.out")
mul_op = block.append_op(
type="mul",
inputs={"X": [mul_x],
"Y": mul_y},
outputs={"Out": [mul_out]},
attrs={"x_num_col_dims": 1})
add_y = block.create_var(
dtype="float32", shape=[5, 8], lod_level=0, name="add.y")
add_out = block.create_var(
dtype="float32", shape=[5, 8], lod_level=0, name="add.out")
add_op = block.append_op(
type="elementwise_add",
inputs={"X": mul_out,
"Y": add_y},
outputs={"Out": add_out},
attrs={"x_num_col_dims": 1})
mean_out = block.create_var(
dtype="float32", shape=[1], lod_level=0, name="mean.out")
block.append_op(
type="mean", inputs={"X": add_out}, outputs={"Out": mean_out})
self.assertEqual(mul_op.idx, 0)
self.assertEqual(add_op.idx, 1)
param_to_grad = prog.append_backward(mean_out, set())
def grad_name(name):
return name + "@GRAD"
for var_name in ("mul.x", "mul.y", "mul.out", "add.y", "add.out",
"mean.out"):
self.assertEqual(param_to_grad[var_name][0], grad_name(var_name))
self.assertEqual(param_to_grad[var_name][1], 0)
expect_ops = [
"mul", "elementwise_add", "mean", "fill_constant", "mean_grad",
"elementwise_add_grad", "mul_grad"
]
actual_ops = []
for op in block.ops:
actual_ops.append(op.type)
self.assertEqual(actual_ops, expect_ops)
def test_append_backward(self):
prog = Program()
block = prog.global_block()
mul_x = block.create_var(dtype="float32",
shape=[5, 10],
lod_level=0,
name="mul.x")
mul_y = block.create_var(dtype="float32",
shape=[10, 8],
lod_level=0,
name="mul.y")
mul_out = block.create_var(dtype="float32",
shape=[5, 8],
lod_level=0,
name="mul.out")
mul_op = block.append_op(type="mul",
inputs={
"X": [mul_x],
"Y": mul_y
},
outputs={"Out": [mul_out]},
attrs={"x_num_col_dims": 1})
add_y = block.create_var(dtype="float32",
shape=[5, 8],
lod_level=0,
name="add.y")
add_out = block.create_var(dtype="float32",
shape=[5, 8],
lod_level=0,
name="add.out")
add_op = block.append_op(type="elementwise_add",
inputs={
"X": mul_out,
"Y": add_y
},
outputs={"Out": add_out},
attrs={"x_num_col_dims": 1})
mean_out = block.create_var(dtype="float32",
shape=[1],
lod_level=0,
name="mean.out")
block.append_op(type="mean",
inputs={"X": add_out},
outputs={"Out": mean_out})
self.assertEqual(mul_op.idx, 0)
self.assertEqual(add_op.idx, 1)
param_to_grad = prog.append_backward(mean_out, set())
def grad_name(name):
return name + "@GRAD"
for var_name in ("mul.x", "mul.y", "mul.out", "add.y", "add.out",
"mean.out"):
self.assertEqual(param_to_grad[var_name][0], grad_name(var_name))
self.assertEqual(param_to_grad[var_name][1], 0)
expect_ops = [
"mul", "elementwise_add", "mean", "fill_constant", "mean_grad",
"elementwise_add_grad", "mul_grad"
]
actual_ops = []
for op in block.ops:
actual_ops.append(op.type)
self.assertEqual(actual_ops, expect_ops)