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import tensorflow as tf
import numpy as np
def get_angles(pos, i, d_model):
angle_rates = 1 / np.power(10000, (2 * (i//2)) / np.float32(d_model))
return pos * angle_rates
def positional_encoding_1d(position, d_model):
angle_rads = get_angles(np.arange(position)[:, np.newaxis],
np.arange(d_model)[np.newaxis, :],
d_model)
# apply sin to even indices in the array; 2i
angle_rads[:, 0::2] = np.sin(angle_rads[:, 0::2])
# apply cos to odd indices in the array; 2i+1
angle_rads[:, 1::2] = np.cos(angle_rads[:, 1::2])
pos_encoding = angle_rads[np.newaxis, ...]
return tf.cast(pos_encoding, dtype=tf.float32)
def positional_encoding_2d(row,col,d_model):
assert d_model % 2 == 0
# first d_model/2 encode row embedding and second d_model/2 encode column embedding
row_pos = np.repeat(np.arange(row),col)[:,np.newaxis]
col_pos = np.repeat(np.expand_dims(np.arange(col),0),row,axis=0).reshape(-1,1)
angle_rads_row = get_angles(row_pos,np.arange(d_model//2)[np.newaxis,:],d_model//2)
angle_rads_col = get_angles(col_pos,np.arange(d_model//2)[np.newaxis,:],d_model//2)
#apply sin and cos to odd and even indices resp.
angle_rads_row[:, 0::2] = np.sin(angle_rads_row[:, 0::2])
angle_rads_row[:, 1::2] = np.cos(angle_rads_row[:, 1::2])
angle_rads_col[:, 0::2] = np.sin(angle_rads_col[:, 0::2])
angle_rads_col[:, 1::2] = np.cos(angle_rads_col[:, 1::2])
pos_encoding = np.concatenate([angle_rads_row,angle_rads_col],axis=1)[np.newaxis, ...]
return tf.cast(pos_encoding, dtype=tf.float32)
def create_padding_mask(seq):
seq = tf.cast(tf.math.equal(seq, 0), tf.float32)
# add extra dimensions to add the padding
# to the attention logits.
return seq[:, tf.newaxis, tf.newaxis, :] # (batch_size, 1, 1, seq_len)
def create_look_ahead_mask(size):
mask = 1 - tf.linalg.band_part(tf.ones((size, size)), -1, 0)
return mask # (seq_len, seq_len)
def create_masks_decoder(tar):
look_ahead_mask = create_look_ahead_mask(tf.shape(tar)[1])
dec_target_padding_mask = create_padding_mask(tar)
combined_mask = tf.maximum(dec_target_padding_mask, look_ahead_mask)
return combined_mask
def scaled_dot_product_attention(q, k, v, mask):
"""Calculate the attention weights.
q, k, v must have matching leading dimensions.
k, v must have matching penultimate dimension, i.e.: seq_len_k = seq_len_v.
The mask has different shapes depending on its type(padding or look ahead)
but it must be broadcastable for addition.
Args:
q: query shape == (..., seq_len_q, depth)
k: key shape == (..., seq_len_k, depth)
v: value shape == (..., seq_len_v, depth_v)
mask: Float tensor with shape broadcastable
to (..., seq_len_q, seq_len_k). Defaults to None.
Returns:
output, attention_weights
"""
matmul_qk = tf.matmul(q, k, transpose_b=True) # (..., seq_len_q, seq_len_k)
# scale matmul_qk
dk = tf.cast(tf.shape(k)[-1], tf.float32)
scaled_attention_logits = matmul_qk / tf.math.sqrt(dk)
# add the mask to the scaled tensor.
if mask is not None:
scaled_attention_logits += (mask * -1e9) #adding -Inf where mask is 1 s.t. value get ignored in softmax
# softmax is normalized on the last axis (seq_len_k) so that the scores
# add up to 1.
attention_weights = tf.nn.softmax(scaled_attention_logits, axis=-1) # (..., seq_len_q, seq_len_k)
output = tf.matmul(attention_weights, v) # (..., seq_len_q, depth_v)
return output, attention_weights |