# 请注意，我们要谈谈神经网络的注意机制和使用方法

01 注意机制是什么？

（用 Matlab 的表示方法），它会改变自己的维度，所以现在[图片上传中。。。（3）]，其中 m≤k。

02 视觉注意

03 硬注意

g = I[y:y+h, x:x+w]

04 软注意

05 高斯注意

def gaussian_mask(u, s, d, R, C):

"""

:param u: tf.Tensor, centre of the first Gaussian.

:param s: tf.Tensor, standard deviation of Gaussians.

:param d: tf.Tensor, shift between Gaussian centres.

:param R: int, number of rows in the mask, there is one Gaussian per row.

:param C: int, number of columns in the mask.

"""

# indices to create centres

R = tf.to_float(tf.reshape(tf.range(R), (1, 1, R)))

C = tf.to_float(tf.reshape(tf.range(C), (1, C, 1)))

centres = u[np.newaxis, :, np.newaxis] + R * d

column_centres = C - centres

mask = tf.exp(-.5 * tf.square(column_centres / s))    # we add eps for numerical stability

def gaussian_glimpse(img_tensor, transform_params, crop_size):

"""

:param img_tensor: tf.Tensor of size (batch_size, Height, Width, channels)

:param transform_params: tf.Tensor of size (batch_size, 6), where params are  (mean_y, std_y, d_y, mean_x, std_x, d_x) specified in pixels.

:param crop_size): tuple of 2 ints, size of the resulting crop

"""

# parse arguments

h, w = crop_size

H, W = img_tensor.shape.as_list()[1:3]

split_ax = transform_params.shape.ndims -1

uy, sy, dy, ux, sx, dx = tf.split(transform_params, 6, split_ax)    # create Gaussian masks, one for each axis

Ay = gaussian_mask(uy, sy, dy, h, H)

Ax = gaussian_mask(ux, sx, dx, w, W)    # extract glimpse

glimpse = tf.matmul(tf.matmul(Ay, img_tensor, adjoint_a=True), Ax)    return glimpse

06 空间变换器

def spatial_transformer(img_tensor, transform_params, crop_size):    """    :param img_tensor: tf.Tensor of size (batch_size, Height, Width, channels)    :param transform_params: tf.Tensor of size (batch_size, 4), where params are  (scale_y, shift_y, scale_x, shift_x)    :param crop_size): tuple of 2 ints, size of the resulting crop    """    constraints = snt.AffineWarpConstraints.no_shear_2d()    img_size = img_tensor.shape.as_list()[1:]    warper = snt.AffineGridWarper(img_size, crop_size, constraints)    grid_coords = warper(transform_params)    glimpse = snt.resampler(img_tensor[..., tf.newaxis], grid_coords)

return glimpse

07 高斯注意vs.空间变换器

08

import tensorflow as tfimport sonnet as sntimport numpy as npimport matplotlib.pyplot as plt

img_size = 10, 10glimpse_size = 5, 5# Create a random image with a squarex = abs(np.random.randn(1, *img_size)) * .3x[0, 3:6, 3:6] = 1crop = x[0, 2:7, 2:7] # contains the square

tf.reset_default_graph()# placeholderstx = tf.placeholder(tf.float32, x.shape, 'image')

tu = tf.placeholder(tf.float32, [1], 'u')

ts = tf.placeholder(tf.float32, [1], 's')

td = tf.placeholder(tf.float32, [1], 'd')

stn_params = tf.placeholder(tf.float32, [1, 4], 'stn_params')

# Gaussian Attentiongaussian_att_params = tf.concat([tu, ts, td, tu, ts, td], -1)

gaussian_glimpssess = tf.Session()# extract a Gaussian glimpseu = 2s = .5d = 1u, s, d = (np.asarray([i]) for i in (u, s, d))

gaussian_crop = sess.run(gaussian_glimpse_expr, feed_dict={tx: x, tu: u, ts: s, td: d})# extract STN glimpsetransform = [.4, -.1, .4, -.1]

transform = np.asarray(transform).reshape((1, 4))

stn_crop = sess.run(stn_glimpse_expr, {tx: x, stn_params: transform})# plotsfig, axes = plt.subplots(1, 4, figsize=(12, 3))

titles = ['Input Image', 'Crop', 'Gaussian Att', 'STN']

imgs = [x, crop, gaussian_crop, stn_crop]for ax, title, img in zip(axes, titles, imgs):

ax.imshow(img.squeeze(), cmap='gray', vmin=0., vmax=1.)

ax.set_title(title)

ax.xaxis.set_visible(False)

ax.yaxis.set_visible(False)e_expr = gaussian_glimpse(tx, gaussian_att_params, glimpse_size)# Spatial Transformerstn_glimpse_expr = spatial_transformer(tx, stn_params, glimpse_size)

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