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HenryAI
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KerasCodeExamples.txt

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def external_attention(
x, dim, num_heads, dim_coefficient=4, attention_dropout=0, projection_dropout=0
):
_, num_patch, channel = x.shape
assert dim % num_heads == 0
num_heads = num_heads * dim_coefficient
x = layers.Dense(dim * dim_coefficient)(x)
# create tensor [batch_size, num_patches, num_heads, dim*dim_coefficient//num_heads]
x = tf.reshape(
x, shape=(-1, num_patch, num_heads, dim * dim_coefficient // num_heads)
)
x = tf.transpose(x, perm=[0, 2, 1, 3])
# a linear layer M_k
attn = layers.Dense(dim // dim_coefficient)(x)
# normalize attention map
attn = layers.Softmax(axis=2)(attn)
# dobule-normalization
attn = attn / (1e-9 + tf.reduce_sum(attn, axis=-1, keepdims=True))
attn = layers.Dropout(attention_dropout)(attn)
# a linear layer M_v
x = layers.Dense(dim * dim_coefficient // num_heads)(attn)
x = tf.transpose(x, perm=[0, 2, 1, 3])
x = tf.reshape(x, [-1, num_patch, dim * dim_coefficient])
# a linear layer to project original dim
x = layers.Dense(dim)(x)
x = layers.Dropout(projection_dropout)(x)
return x
Implement the MLP block
def mlp(x, embedding_dim, mlp_dim, drop_rate=0.2):
x = layers.Dense(mlp_dim, activation=tf.nn.gelu)(x)
x = layers.Dropout(drop_rate)(x)
x = layers.Dense(embedding_dim)(x)
x = layers.Dropout(drop_rate)(x)
return x
Implement the Transformer block
def transformer_encoder(
x,
embedding_dim,
mlp_dim,
num_heads,
dim_coefficient,
attention_dropout,
projection_dropout,
attention_type=\"external_attention\",
):
residual_1 = x
x = layers.LayerNormalization(epsilon=1e-5)(x)
if attention_type == \"external_attention\":
x = external_attention(
x,
embedding_dim,
num_heads,
dim_coefficient,
attention_dropout,
projection_dropout,
)
elif attention_type == \"self_attention\":
x = layers.MultiHeadAttention(
num_heads=num_heads, key_dim=embedding_dim, dropout=attention_dropout
)(x, x)
x = layers.add([x, residual_1])
residual_2 = x
x = layers.LayerNormalization(epsilon=1e-5)(x)
x = mlp(x, embedding_dim, mlp_dim)
x = layers.add([x, residual_2])
return x
Implement the EANet model
The EANet model leverages external attention. The computational complexity of traditional self attention is O(d * N ** 2), where d is the embedding size, and N is the number of patch. the authors find that most pixels are closely related to just a few other pixels, and an N-to-N attention matrix may be redundant. So, t...
def get_model(attention_type=\"external_attention\"):
inputs = layers.Input(shape=input_shape)
# Image augment
x = data_augmentation(inputs)
# Extract patches.
x = PatchExtract(patch_size)(x)
# Create patch embedding.
x = PatchEmbedding(num_patches, embedding_dim)(x)
# Create Transformer block.
for _ in range(num_transformer_blocks):
x = transformer_encoder(
x,
embedding_dim,
mlp_dim,
num_heads,
dim_coefficient,
attention_dropout,
projection_dropout,
attention_type,
)
x = layers.GlobalAvgPool1D()(x)
outputs = layers.Dense(num_classes, activation=\"softmax\")(x)
model = keras.Model(inputs=inputs, outputs=outputs)
return model
Train on CIFAR-100
model = get_model(attention_type=\"external_attention\")
model.compile(
loss=keras.losses.CategoricalCrossentropy(label_smoothing=label_smoothing),