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model.py
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import tensorflow as tf
import time
import numpy as np
import matplotlib.pyplot as plt
def gelu(x):
return 0.5 * x * (1.0 + tf.math.erf(x / tf.sqrt(2.)))
# 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(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 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 scaled_dot_product_attention(q, k, v, mask,adjoin_matrix):
"""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)
if adjoin_matrix is not None:
scaled_attention_logits += adjoin_matrix
# 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
class MultiHeadAttention(tf.keras.layers.Layer):
def __init__(self, d_model, num_heads):
super(MultiHeadAttention, self).__init__()
self.num_heads = num_heads
self.d_model = d_model
assert d_model % self.num_heads == 0
self.depth = d_model // self.num_heads
self.wq = tf.keras.layers.Dense(d_model)
self.wk = tf.keras.layers.Dense(d_model)
self.wv = tf.keras.layers.Dense(d_model)
self.dense = tf.keras.layers.Dense(d_model)
def split_heads(self, x, batch_size):
"""Split the last dimension into (num_heads, depth).
Transpose the result such that the shape is (batch_size, num_heads, seq_len, depth)
"""
x = tf.reshape(x, (batch_size, -1, self.num_heads, self.depth))
return tf.transpose(x, perm=[0, 2, 1, 3])
def call(self, v, k, q, mask,adjoin_matrix):
batch_size = tf.shape(q)[0]
q = self.wq(q) # (batch_size, seq_len, d_model)
k = self.wk(k) # (batch_size, seq_len, d_model)
v = self.wv(v) # (batch_size, seq_len, d_model)
q = self.split_heads(q, batch_size) # (batch_size, num_heads, seq_len_q, depth)
k = self.split_heads(k, batch_size) # (batch_size, num_heads, seq_len_k, depth)
v = self.split_heads(v, batch_size) # (batch_size, num_heads, seq_len_v, depth)
# scaled_attention.shape == (batch_size, num_heads, seq_len_q, depth)
# attention_weights.shape == (batch_size, num_heads, seq_len_q, seq_len_k)
scaled_attention, attention_weights = scaled_dot_product_attention(
q, k, v, mask,adjoin_matrix)
scaled_attention = tf.transpose(scaled_attention,
perm=[0, 2, 1, 3]) # (batch_size, seq_len_q, num_heads, depth)
concat_attention = tf.reshape(scaled_attention,
(batch_size, -1, self.d_model)) # (batch_size, seq_len_q, d_model)
output = self.dense(concat_attention) # (batch_size, seq_len_q, d_model)
return output, attention_weights
def point_wise_feed_forward_network(d_model, dff):
return tf.keras.Sequential([
tf.keras.layers.Dense(dff, activation=gelu), # (batch_size, seq_len, dff)tf.keras.layers.LeakyReLU(0.01)
tf.keras.layers.Dense(d_model) # (batch_size, seq_len, d_model)
])
class EncoderLayer(tf.keras.layers.Layer):
def __init__(self, d_model, num_heads, dff, rate=0.1):
super(EncoderLayer, self).__init__()
self.mha = MultiHeadAttention(d_model, num_heads)
self.ffn = point_wise_feed_forward_network(d_model, dff)
self.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=1e-6)
self.layernorm2 = tf.keras.layers.LayerNormalization(epsilon=1e-6)
self.dropout1 = tf.keras.layers.Dropout(rate)
self.dropout2 = tf.keras.layers.Dropout(rate)
def call(self, x, training, mask,adjoin_matrix):
attn_output, attention_weights = self.mha(x, x, x, mask,adjoin_matrix) # (batch_size, input_seq_len, d_model)
attn_output = self.dropout1(attn_output, training=training)
out1 = self.layernorm1(x + attn_output) # (batch_size, input_seq_len, d_model)
ffn_output = self.ffn(out1) # (batch_size, input_seq_len, d_model)
ffn_output = self.dropout2(ffn_output, training=training)
out2 = self.layernorm2(out1 + ffn_output) # (batch_size, input_seq_len, d_model)
return out2,attention_weights
# class EmbeddingDense(tf.keras.layers.Layer):
# """运算跟Dense一致，只不过kernel用Embedding层的embedding矩阵
# """
#
# def __init__(self,embedding_layer, activation=None, **kwargs):
# super(EmbeddingDense, self).__init__(**kwargs)
# self.activation = activation
# self.units = embedding_layer.input_dim
# self.embedding_layer = embedding_layer
# self.activation = tf.keras.layers.Activation(self.activation)
#
#
# def build(self, input_shape):
# super(EmbeddingDense, self).build(input_shape)
# self.kernel = tf.transpose(self.embedding_layer.embeddings)
# self.bias = self.add_weight(name='bias',
# shape=(self.units,),
# initializer='zeros')
#
# def call(self, inputs):
# outputs = tf.matmul(inputs, self.kernel)
# outputs = outputs+self.bias
# outputs = self.activation(outputs)
# return outputs
#
# def compute_output_shape(self, input_shape):
# return input_shape[:-1] + (self.units,)
class Encoder(tf.keras.Model):
def __init__(self, num_layers, d_model, num_heads, dff, input_vocab_size,
maximum_position_encoding, rate=0.1):
super(Encoder, self).__init__()
self.d_model = d_model
self.num_layers = num_layers
self.embedding = tf.keras.layers.Embedding(input_vocab_size, d_model)
# self.pos_encoding = positional_encoding(maximum_position_encoding,
# self.d_model)
self.enc_layers = [EncoderLayer(d_model, num_heads, dff, rate)
for _ in range(num_layers)]
self.dropout = tf.keras.layers.Dropout(rate)
def call(self, x, training, mask,adjoin_matrix):
seq_len = tf.shape(x)[1]
adjoin_matrix = adjoin_matrix[:,tf.newaxis,:,:]
# adding embedding and position encoding.
x = self.embedding(x) # (batch_size, input_seq_len, d_model)
x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))
x = self.dropout(x, training=training)
for i in range(self.num_layers):
x,attention_weights = self.enc_layers[i](x, training, mask,adjoin_matrix)
return x # (batch_size, input_seq_len, d_model)
class Encoder_test(tf.keras.Model):
def __init__(self, num_layers, d_model, num_heads, dff, input_vocab_size,
maximum_position_encoding, rate=0.1):
super(Encoder_test, self).__init__()
self.d_model = d_model
self.num_layers = num_layers
self.embedding = tf.keras.layers.Embedding(input_vocab_size, d_model)
# self.pos_encoding = positional_encoding(maximum_position_encoding,
# self.d_model)
self.enc_layers = [EncoderLayer(d_model, num_heads, dff, rate)
for _ in range(num_layers)]
self.dropout = tf.keras.layers.Dropout(rate)
def call(self, x, training, mask,adjoin_matrix):
seq_len = tf.shape(x)[1]
adjoin_matrix = adjoin_matrix[:,tf.newaxis,:,:]
# adding embedding and position encoding.
x = self.embedding(x) # (batch_size, input_seq_len, d_model)
x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))
# x += self.pos_encoding[:, :seq_len, :]
x = self.dropout(x, training=training)
attention_weights_list = []
xs = []
for i in range(self.num_layers):
x,attention_weights = self.enc_layers[i](x, training, mask,adjoin_matrix)
attention_weights_list.append(attention_weights)
xs.append(x)
return x,attention_weights_list,xs
class BertModel_test(tf.keras.Model):
def __init__(self,num_layers = 6,d_model = 256,dff = 512,num_heads = 8,vocab_size = 17,dropout_rate = 0.1):
super(BertModel_test, self).__init__()
self.encoder = Encoder_test(num_layers=num_layers,d_model=d_model,
num_heads=num_heads,dff=dff,input_vocab_size=vocab_size,maximum_position_encoding=200,rate=dropout_rate)
self.fc1 = tf.keras.layers.Dense(d_model, activation=gelu)
self.layernorm = tf.keras.layers.LayerNormalization(-1)
self.fc2 = tf.keras.layers.Dense(vocab_size)
def call(self,x,adjoin_matrix,mask,training=False):
x,att,xs = self.encoder(x,training=training,mask=mask,adjoin_matrix=adjoin_matrix)
x = self.fc1(x)
x = self.layernorm(x)
x = self.fc2(x)
return x,att,xs
class BertModel(tf.keras.Model):
def __init__(self,num_layers = 6,d_model = 256,dff = 512,num_heads = 8,vocab_size = 17,dropout_rate = 0.1):
super(BertModel, self).__init__()
self.encoder = Encoder(num_layers=num_layers,d_model=d_model,
num_heads=num_heads,dff=dff,input_vocab_size=vocab_size,maximum_position_encoding=200,rate=dropout_rate)
self.fc1 = tf.keras.layers.Dense(d_model, activation=gelu)
self.layernorm = tf.keras.layers.LayerNormalization(-1)
self.fc2 = tf.keras.layers.Dense(vocab_size)
def call(self,x,adjoin_matrix,mask,training=False):
x = self.encoder(x,training=training,mask=mask,adjoin_matrix=adjoin_matrix)
x = self.fc1(x)
x = self.layernorm(x)
x = self.fc2(x)
return x
class PredictModel(tf.keras.Model):
def __init__(self,num_layers = 6,d_model = 256,dff = 512,num_heads = 8,vocab_size =17,dropout_rate = 0.1,dense_dropout=0.1):
super(PredictModel, self).__init__()
self.encoder = Encoder(num_layers=num_layers,d_model=d_model,
num_heads=num_heads,dff=dff,input_vocab_size=vocab_size,maximum_position_encoding=200,rate=dropout_rate)
self.fc1 = tf.keras.layers.Dense(256,activation=tf.keras.layers.LeakyReLU(0.1))
self.dropout = tf.keras.layers.Dropout(dense_dropout)
self.fc2 = tf.keras.layers.Dense(1)
def call(self,x,adjoin_matrix,mask,training=False):
x = self.encoder(x,training=training,mask=mask,adjoin_matrix=adjoin_matrix)
x = x[:,0,:]
x = self.fc1(x)
x = self.dropout(x,training=training)
x = self.fc2(x)
return x
class PredictModel_test(tf.keras.Model):
def __init__(self,num_layers = 6,d_model = 256,dff = 512,num_heads = 8,vocab_size =17,dropout_rate = 0.1,dense_dropout=0.5):
super(PredictModel_test, self).__init__()
self.encoder = Encoder_test(num_layers=num_layers,d_model=d_model,
num_heads=num_heads,dff=dff,input_vocab_size=vocab_size,maximum_position_encoding=200,rate=dropout_rate)
self.fc1 = tf.keras.layers.Dense(256, activation=tf.keras.layers.LeakyReLU(0.1))
self.dropout = tf.keras.layers.Dropout(dense_dropout)
self.fc2 = tf.keras.layers.Dense(1)
def call(self,x,adjoin_matrix,mask,training=False):
x,att,xs = self.encoder(x,training=training,mask=mask,adjoin_matrix=adjoin_matrix)
x = x[:, 0, :]
x = self.fc1(x)
x = self.dropout(x, training=training)
x = self.fc2(x)
return x,att,xs