PyTorch教程2.5之自动微分

x = np.arange(4.0)
x

array([0., 1., 2., 3.])

Before we calculate the gradient of y with respect to x, we need a place to store it. In general, we avoid allocating new memory every time we take a derivative because deep learning requires successively computing derivatives with respect to the same parameters thousands or millions of times, and we might risk running out of memory. Note that the gradient of a scalar-valued function with respect to a vector x is vector-valued and has the same shape as x.

# We allocate memory for a tensor's gradient by invoking `attach_grad`
x.attach_grad()
# After we calculate a gradient taken with respect to `x`, we will be able to
# access it via the `grad` attribute, whose values are initialized with 0s
x.grad

array([0., 0., 0., 0.])

x = tf.range(4, dtype=tf.float32)
x

<tf.Tensor: shape=(4,), dtype=float32, numpy=array([0., 1., 2., 3.], dtype=float32)>

Before we calculate the gradient of y with respect to x, we need a place to store it. In general, we avoid allocating new memory every time we take a derivative because deep learning requires successively computing derivatives with respect to the same parameters thousands or millions of times, and we might risk running out of memory. Note that the gradient of a scalar-valued function with respect to a vector x is vector-valued and has the same shape as x.

x = tf.Variable(x)

# Our code is inside an `autograd.record` scope to build the computational
# graph
with autograd.record():
  y = 2 * np.dot(x, x)
y

array(28.)

We can now take the gradient of y with respect to x by calling its backward method. Next, we can access the gradient via x’s grad attribute.

y.backward()
x.grad

[09:38:36] src/base.cc:49: GPU context requested, but no GPUs found.

array([ 0., 4., 8., 12.])

y = lambda x: 2 * jnp.dot(x, x)
y(x)

Array(28., dtype=float32)

We can now take the gradient of y with respect to x by passing through the grad transform.

from jax import grad

# The `grad` transform returns a Python function that
# computes the gradient of the original function
x_grad = grad(y)(x)
x_grad

Array([ 0., 4., 8., 12.], dtype=float32)

# Record all computations onto a tape
with tf.GradientTape() as t:
  y = 2 * tf.tensordot(x, x, axes=1)
y

<tf.Tensor: shape=(), dtype=float32, numpy=28.0>

We can now calculate the gradient of y with respect to x by calling the gradient method.

x_grad = t.gradient(y, x)
x_grad

<tf.Tensor: shape=(4,), dtype=float32, numpy=array([