1、多頭注意力機制層的構建

class MultiHeadAttention(tf.keras.layers.Layer):def __init__(self, num_heads, d_model):super(MultiHeadAttention, self).__init__()self.num_heads = num_headsself.d_model = d_modelassert d_model % self.num_heads == 0self.depth = d_model // self.num_headsself.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 scaled_dot_product_attention(self, 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_q# scale matmul_qkdk = 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) # 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_weightsdef call(self, v, k, q, mask):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_# split headsq = 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 dot product attentionscaled_attention, attention_weights = self.scaled_dot_product_attention(q, k, v, mask)# concatenation of headsscaled_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)# final linear layeroutput = self.dense(concat_attention) # (batch_size, seq_len_q, d_model)return output

2、構建股價預測模型

# Stock price prediction model class StockPricePredictionModel(tf.keras.Model):def __init__(self, num_heads, d_model, num_lstm_units):super(StockPricePredictionModel, self).__init__()self.num_heads = num_headsself.f_model = d_modelself.num_lstm_units = num_lstm_unitsself.multi_head_attention = MultiHeadAttention(self.num_heads, self.d_model)self.lstm = tf.keras.layers.LSTM(self.num_lstm_units, return_sequences=True)self.dense = tf.keras.layers.Dense(1)def call(self, inputs, mask):attention_output = self.multi_head_attention(inputs, input, input, mask)lstm_output = self.lstm(attention_output)prediction = self.dense(lstm_output)return predictionmodel = StockPricePredictionModel(num_heads=9, d_model=256, num_lstm_units=128)

3、模型訓練與結果

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總結

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