def test_grad_of_grad():
x2 = ad.Variable(name="x2")
x3 = ad.Variable(name="x3")
y = x2 * x2 + x2 * x3
grad_x2, grad_x3 = ad.gradients(y, [x2, x3])
grad_x2_x2, grad_x2_x3 = ad.gradients(grad_x2, [x2, x3])
executor = ad.Executor([y, grad_x2, grad_x3, grad_x2_x2, grad_x2_x3])
x2_val = 2 * np.ones(3)
x3_val = 3 * np.ones(3)
y_val, grad_x2_val, grad_x3_val, grad_x2_x2_val, grad_x2_x3_val = executor.run(
feed_dict={x2: x2_val, x3: x3_val}
)
expected_yval = x2_val * x2_val + x2_val * x3_val
expected_grad_x2_val = 2 * x2_val + x3_val
expected_grad_x3_val = x2_val
expected_grad_x2_x2_val = 2 * np.ones_like(x2_val)
expected_grad_x2_x3_val = 1 * np.ones_like(x2_val)
assert isinstance(y, ad.Node)
assert np.array_equal(y_val, expected_yval)
assert np.array_equal(grad_x2_val, expected_grad_x2_val)
assert np.array_equal(grad_x3_val, expected_grad_x3_val)
assert np.array_equal(grad_x2_x2_val, expected_grad_x2_x2_val)
assert np.array_equal(grad_x2_x3_val, expected_grad_x2_x3_val)
def test_lr():
W = ad.Variable(name="W")
b = ad.Variable(name="b")
X = ad.Variable(name="X")
y_ = ad.Variable(name="y_")
z = ad.matmul_op(X, W) + b
loss = ad.sigmoidcrossentropy_op(z, y_)
grad_W, grad_b = ad.gradients(loss, [W, b])
def test_mul_two_vars():
x2 = ad.Variable(name="x2")
x3 = ad.Variable(name="x3")
y = x2 * x3
grad_x2, grad_x3 = ad.gradients(y, [x2, x3])
executor = ad.Executor([y, grad_x2, grad_x3])
x2_val = 2 * np.ones(3)
x3_val = 3 * np.ones(3)
y_val, grad_x2_val, grad_x3_val = executor.run(feed_dict={x2: x2_val, x3: x3_val})
assert isinstance(y, ad.Node)
assert np.array_equal(y_val, x2_val * x3_val)
assert np.array_equal(grad_x2_val, x3_val)
assert np.array_equal(grad_x3_val, x2_val)
def test_add_mul_mix_1():
x1 = ad.Variable(name="x1")
x2 = ad.Variable(name="x2")
x3 = ad.Variable(name="x3")
y = x1 + x2 * x3 * x1
grad_x1, grad_x2, grad_x3 = ad.gradients(y, [x1, x2, x3])
executor = ad.Executor([y, grad_x1, grad_x2, grad_x3])
x1_val = 1 * np.ones(3)
x2_val = 2 * np.ones(3)
x3_val = 3 * np.ones(3)
y_val, grad_x1_val, grad_x2_val, grad_x3_val = executor.run(feed_dict={x1: x1_val, x2: x2_val, x3: x3_val})
assert isinstance(y, ad.Node)
assert np.array_equal(y_val, x1_val + x2_val * x3_val)
assert np.array_equal(grad_x1_val, np.ones_like(x1_val) + x2_val * x3_val)
assert np.array_equal(grad_x2_val, x3_val * x1_val)
assert np.array_equal(grad_x3_val, x2_val * x1_val)
def test_exp_grad():
x = ad.Variable("x")
y = ad.exp_op(x)
x_grad, = ad.gradients(y, [x])
executor = ad.Executor([y, x_grad])
x_val = 1
y_val, x_grad_val = executor.run(feed_dict={x: x_val})
print(y_val)
print(x_grad_val)
def test_matmul_two_vars():
x2 = ad.Variable(name="x2")
x3 = ad.Variable(name="x3")
y = ad.matmul_op(x2, x3)
grad_x2, grad_x3 = ad.gradients(y, [x2, x3])
executor = ad.Executor([y, grad_x2, grad_x3])
x2_val = np.array([[1, 2], [3, 4], [5, 6]]) # 3x2
x3_val = np.array([[7, 8, 9], [10, 11, 12]]) # 2x3
y_val, grad_x2_val, grad_x3_val = executor.run(feed_dict={x2: x2_val, x3: x3_val})
expected_yval = np.matmul(x2_val, x3_val)
expected_grad_x2_val = np.matmul(np.ones_like(expected_yval), np.transpose(x3_val))
expected_grad_x3_val = np.matmul(np.transpose(x2_val), np.ones_like(expected_yval))
assert isinstance(y, ad.Node)
assert np.array_equal(y_val, expected_yval)
assert np.array_equal(grad_x2_val, expected_grad_x2_val)
assert np.array_equal(grad_x3_val, expected_grad_x3_val)
def test_add_mul_mix_3():
x2 = ad.Variable(name="x2")
x3 = ad.Variable(name="x3")
z = x2 * x2 + x2 + x3 + 3
y = z * z + x3
grad_x2, grad_x3 = ad.gradients(y, [x2, x3])
executor = ad.Executor([y, grad_x2, grad_x3])
x2_val = 2 * np.ones(3)
x3_val = 3 * np.ones(3)
y_val, grad_x2_val, grad_x3_val = executor.run(feed_dict={x2: x2_val, x3: x3_val})
z_val = x2_val * x2_val + x2_val + x3_val + 3
expected_yval = z_val * z_val + x3_val
expected_grad_x2_val = 2 * (x2_val * x2_val + x2_val + x3_val + 3) * (2 * x2_val + 1)
expected_grad_x3_val = 2 * (x2_val * x2_val + x2_val + x3_val + 3) + 1
assert isinstance(y, ad.Node)
assert np.array_equal(y_val, expected_yval)
assert np.array_equal(grad_x2_val, expected_grad_x2_val)
assert np.array_equal(grad_x3_val, expected_grad_x3_val)
def test_identity():
x2 = ad.Variable(name="x2")
y = x2
grad_x2, = ad.gradients(y, [x2])
executor = ad.Executor([y, grad_x2])
x2_val = 2 * np.ones(3)
y_val, grad_x2_val = executor.run(feed_dict={x2: x2_val})
assert isinstance(y, ad.Node)
assert np.array_equal(y_val, x2_val)
assert np.array_equal(grad_x2_val, np.ones_like(x2_val))
def test_exp():
x1 = ad.Variable("x1")
x2 = ad.exp_op(x1)
x3 = x2 + 1
x4 = x2 * x3
x1_grad, = ad.gradients(x4, [x1])
executor = ad.Executor([x4])
x1_val = 1
x4_val, x1_grad = executor.run(feed_dict={x1: x1_val})
print(x4_val)
print(x1_grad)
def mnist_logreg(executor_ctx=None,
num_epochs=10,
print_loss_val_each_epoch=False):
print("Build logistic regression model...")
W1 = ad.Variable(name="W1")
b1 = ad.Variable(name="b1")
X = ad.Variable(name="X")
y_ = ad.Variable(name="y_")
z1 = ad.matmul_op(X, W1)
y = z1 + ad.broadcastto_op(b1, z1)
loss = ad.softmaxcrossentropy_op(y, y_)
grad_W1, grad_b1 = ad.gradients(loss, [W1, b1])
executor = ad.Executor([loss, grad_W1, grad_b1, y], ctx=executor_ctx)
# Read input data
datasets = load_mnist_data("mnist.pkl.gz")
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]
# Set up minibatch
batch_size = 1000
n_train_batches = train_set_x.shape[0] // batch_size
n_valid_batches = valid_set_x.shape[0] // batch_size
print("Start training loop...")
# Initialize parameters
W1_val = np.zeros((784, 10))
b1_val = np.zeros((10))
X_val = np.empty(shape=(batch_size, 784), dtype=np.float32)
y_val = np.empty(shape=(batch_size, 10), dtype=np.float32)
valid_X_val = np.empty(shape=(batch_size, 784), dtype=np.float32)
valid_y_val = np.empty(shape=(batch_size, 10), dtype=np.float32)
if ndarray.is_gpu_ctx(executor_ctx):
W1_val = ndarray.array(W1_val, ctx=executor_ctx)
b1_val = ndarray.array(b1_val, ctx=executor_ctx)
X_val = ndarray.array(X_val, ctx=executor_ctx)
y_val = ndarray.array(y_val, ctx=executor_ctx)
lr = 1e-3
for i in range(num_epochs):
print("epoch %d" % i)
for minibatch_index in range(n_train_batches):
minibatch_start = minibatch_index * batch_size
minibatch_end = (minibatch_index + 1) * batch_size
X_val[:] = train_set_x[minibatch_start:minibatch_end]
y_val[:] = convert_to_one_hot(
train_set_y[minibatch_start:minibatch_end])
loss_val, grad_W1_val, grad_b1_val, _ = executor.run(feed_dict={
X: X_val,
y_: y_val,
W1: W1_val,
b1: b1_val
})
# SGD update
if (executor_ctx is None):
W1_val = W1_val - lr * grad_W1_val
b1_val = b1_val - lr * grad_b1_val
else:
sgd_update_gpu(W1_val, grad_W1_val, lr)
sgd_update_gpu(b1_val, grad_b1_val, lr)
if print_loss_val_each_epoch:
if isinstance(loss_val, ndarray.NDArray):
print(loss_val.asnumpy())
else:
print(loss_val)
correct_predictions = []
for minibatch_index in range(n_valid_batches):
minibatch_start = minibatch_index * batch_size
minibatch_end = (minibatch_index + 1) * batch_size
valid_X_val[:] = valid_set_x[minibatch_start:minibatch_end]
valid_y_val[:] = convert_to_one_hot(
valid_set_y[minibatch_start:minibatch_end])
_, _, _, valid_y_predicted = executor.run(
feed_dict={
X: valid_X_val,
y_: valid_y_val,
W1: W1_val,
b1: b1_val
},
convert_to_numpy_ret_vals=True)
correct_prediction = np.equal(np.argmax(valid_y_val, 1),
np.argmax(valid_y_predicted,
1)).astype(np.float)
correct_predictions.extend(correct_prediction)
accuracy = np.mean(correct_predictions)
# validation set accuracy=0.928200
print("validation set accuracy=%f" % accuracy)
def mnist_mlp(executor_ctx=None,
num_epochs=10,
print_loss_val_each_epoch=False):
print("Build 3-layer MLP model...")
W1 = ad.Variable(name="W1")
W2 = ad.Variable(name="W2")
W3 = ad.Variable(name="W3")
b1 = ad.Variable(name="b1")
b2 = ad.Variable(name="b2")
b3 = ad.Variable(name="b3")
X = ad.Variable(name="X")
y_ = ad.Variable(name="y_")
# relu(X W1+b1)
z1 = ad.matmul_op(X, W1)
z2 = z1 + ad.broadcastto_op(b1, z1)
z3 = ad.relu_op(z2)
# relu(z3 W2+b2)
z4 = ad.matmul_op(z3, W2)
z5 = z4 + ad.broadcastto_op(b2, z4)
z6 = ad.relu_op(z5)
# softmax(z5 W2+b2)
z7 = ad.matmul_op(z6, W3)
y = z7 + ad.broadcastto_op(b3, z7)
loss = ad.softmaxcrossentropy_op(y, y_)
grad_W1, grad_W2, grad_W3, grad_b1, grad_b2, grad_b3 = ad.gradients(
loss, [W1, W2, W3, b1, b2, b3])
executor = ad.Executor(
[loss, grad_W1, grad_W2, grad_W3, grad_b1, grad_b2, grad_b3, y],
ctx=executor_ctx)
# Read input data
datasets = load_mnist_data("mnist.pkl.gz")
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]
# Set up minibatch
batch_size = 1000
n_train_batches = train_set_x.shape[0] // batch_size
n_valid_batches = valid_set_x.shape[0] // batch_size
print("Start training loop...")
# Initialize parameters
rand = np.random.RandomState(seed=123)
W1_val = rand.normal(scale=0.1, size=(784, 256))
W2_val = rand.normal(scale=0.1, size=(256, 100))
W3_val = rand.normal(scale=0.1, size=(100, 10))
b1_val = rand.normal(scale=0.1, size=(256))
b2_val = rand.normal(scale=0.1, size=(100))
b3_val = rand.normal(scale=0.1, size=(10))
X_val = np.empty(shape=(batch_size, 784), dtype=np.float32)
y_val = np.empty(shape=(batch_size, 10), dtype=np.float32)
valid_X_val = np.empty(shape=(batch_size, 784), dtype=np.float32)
valid_y_val = np.empty(shape=(batch_size, 10), dtype=np.float32)
if ndarray.is_gpu_ctx(executor_ctx):
W1_val = ndarray.array(W1_val, ctx=executor_ctx)
W2_val = ndarray.array(W2_val, ctx=executor_ctx)
W3_val = ndarray.array(W3_val, ctx=executor_ctx)
b1_val = ndarray.array(b1_val, ctx=executor_ctx)
b2_val = ndarray.array(b2_val, ctx=executor_ctx)
b3_val = ndarray.array(b3_val, ctx=executor_ctx)
X_val = ndarray.array(X_val, ctx=executor_ctx)
y_val = ndarray.array(y_val, ctx=executor_ctx)
lr = 1.0e-3
for i in range(num_epochs):
print("epoch %d" % i)
for minibatch_index in range(n_train_batches):
minibatch_start = minibatch_index * batch_size
minibatch_end = (minibatch_index + 1) * batch_size
X_val[:] = train_set_x[minibatch_start:minibatch_end]
y_val[:] = convert_to_one_hot(
train_set_y[minibatch_start:minibatch_end])
loss_val, grad_W1_val, grad_W2_val, grad_W3_val, \
grad_b1_val, grad_b2_val, grad_b3_val, _ = executor.run(
feed_dict={
X: X_val,
y_: y_val,
W1: W1_val,
W2: W2_val,
W3: W3_val,
b1: b1_val,
b2: b2_val,
b3: b3_val})
# SGD update
if (executor_ctx is None):
W1_val = W1_val - lr * grad_W1_val
W2_val = W2_val - lr * grad_W2_val
W3_val = W3_val - lr * grad_W3_val
b1_val = b1_val - lr * grad_b1_val
b2_val = b2_val - lr * grad_b2_val
b3_val = b3_val - lr * grad_b3_val
else:
sgd_update_gpu(W1_val, grad_W1_val, lr)
sgd_update_gpu(W2_val, grad_W2_val, lr)
sgd_update_gpu(W3_val, grad_W3_val, lr)
sgd_update_gpu(b1_val, grad_b1_val, lr)
sgd_update_gpu(b2_val, grad_b2_val, lr)
sgd_update_gpu(b3_val, grad_b3_val, lr)
if print_loss_val_each_epoch:
if isinstance(loss_val, ndarray.NDArray):
print(loss_val.asnumpy())
else:
print(loss_val)
correct_predictions = []
for minibatch_index in range(n_valid_batches):
minibatch_start = minibatch_index * batch_size
minibatch_end = (minibatch_index + 1) * batch_size
valid_X_val[:] = valid_set_x[minibatch_start:minibatch_end]
valid_y_val[:] = convert_to_one_hot(
valid_set_y[minibatch_start:minibatch_end])
_, _, _, _, _, _, _, valid_y_predicted = executor.run(
feed_dict={
X: valid_X_val,
y_: valid_y_val,
W1: W1_val,
W2: W2_val,
W3: W3_val,
b1: b1_val,
b2: b2_val,
b3: b3_val
},
convert_to_numpy_ret_vals=True)
correct_prediction = np.equal(np.argmax(valid_y_val, 1),
np.argmax(valid_y_predicted,
1)).astype(np.float)
correct_predictions.extend(correct_prediction)
accuracy = np.mean(correct_predictions)
# validation set accuracy=0.970800
print("validation set accuracy=%f" % accuracy)