def __init__(self,
d_model,
heads,
d_ff=1024,
dropout=0.1,
attention_type="Baseline",
relative_time_pitch=False,
max_relative_position=512):
super().__init__()
self.norm_1 = Norm(d_model)
self.norm_2 = Norm(d_model)
self.norm_3 = Norm(d_model)
self.attention_type = attention_type
self.relative_time_pitch = relative_time_pitch
self.dropout_1 = nn.Dropout(dropout)
self.dropout_2 = nn.Dropout(dropout)
self.dropout_3 = nn.Dropout(dropout)
self.attn_1 = MultiHeadAttention(heads, d_model, dropout = dropout, attention_type = self.attention_type, \
relative_time_pitch = self.relative_time_pitch,
max_relative_position = max_relative_position)
self.attn_2 = MultiHeadAttention(heads, d_model, dropout =dropout, attention_type = self.attention_type, \
relative_time_pitch = self.relative_time_pitch,
max_relative_position = max_relative_position)
self.ff = FeedForward(d_model, d_ff, dropout)
def __init__(self, d_model, heads, dropout=0.1):
super().__init__()
self.norm_1 = Norm(d_model)
self.norm_2 = Norm(d_model)
self.norm_3 = Norm(d_model)
self.dropout_1 = nn.Dropout(dropout)
self.dropout_2 = nn.Dropout(dropout)
self.dropout_3 = nn.Dropout(dropout)
self.attn_1 = MultiHeadAttention(heads, d_model, dropout=dropout)
self.attn_2 = MultiHeadAttention(heads, d_model, dropout=dropout)
self.ff = FeedForward(d_model, dropout=dropout)
def __init__(self, d_model, num_heads, dff, rate=0.1):
super(DecoderLayer, self).__init__()
self.mha1 = MultiHeadAttention(d_model, num_heads)
self.mha2 = 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.layernorm3 = tf.keras.layers.LayerNormalization(epsilon=1e-6)
self.dropout1 = tf.keras.layers.Dropout(rate)
self.dropout2 = tf.keras.layers.Dropout(rate)
self.dropout3 = tf.keras.layers.Dropout(rate)
def __init__(self, d_input, d_model, heads, dropout=0.1):
super().__init__()
self.input_linear = nn.Linear(d_input, d_model)
self.norm_1 = nn.LayerNorm(d_model)
self.norm_2 = nn.LayerNorm(d_model)
self.attn = MultiHeadAttention(heads, d_model, dropout=dropout)
self.ff = FeedForward(d_model, dropout=dropout)
self.dropout_1 = nn.Dropout(dropout)
self.dropout_2 = nn.Dropout(dropout)
