Spaces:
Sleeping
Sleeping
File size: 7,419 Bytes
b8c16a3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 | import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
class CNN_Encoder(keras.Model):
def __init__(self, embedding_dim=256):
super(CNN_Encoder, self).__init__()
self.conv1 = layers.Conv2D(
filters=32,
kernel_size=3,
strides=2,
padding='same',
name='conv_block1'
)
self.bn1 = layers.BatchNormalization(name='bn1')
self.leaky1 = layers.LeakyReLU(alpha=0.2, name='leaky1')
self.pool1 = layers.MaxPool2D(pool_size=2, strides=2, padding='same', name='pool1')
self.conv2 = layers.Conv2D(
filters=64,
kernel_size=3,
strides=2,
padding='same',
name='conv_block2'
)
self.bn2 = layers.BatchNormalization(name='bn2')
self.leaky2 = layers.LeakyReLU(alpha=0.2, name='leaky2')
self.pool2 = layers.MaxPool2D(pool_size=2, strides=2, padding='same', name='pool2')
self.conv3 = layers.Conv2D(
filters=128,
kernel_size=3,
strides=2,
padding='same',
name='conv_block3'
)
self.bn3 = layers.BatchNormalization(name='bn3')
self.leaky3 = layers.LeakyReLU(alpha=0.2, name='leaky3')
self.pool3 = layers.MaxPool2D(pool_size=2, strides=2, padding='same', name='pool3')
self.conv4 = layers.Conv2D(
filters=256,
kernel_size=3,
strides=2,
padding='same',
name='conv_block4'
)
self.bn4 = layers.BatchNormalization(name='bn4')
self.leaky4 = layers.LeakyReLU(alpha=0.2, name='leaky4')
self.reshape = layers.Reshape((-1, 256), name='reshape_features')
self.fc = layers.Dense(embedding_dim, activation='relu', name='fc_projection')
self.embedding_dim = embedding_dim
def call(self, x, training=False):
x = self.conv1(x)
x = self.bn1(x, training=training)
x = self.leaky1(x)
x = self.pool1(x)
x = self.conv2(x)
x = self.bn2(x, training=training)
x = self.leaky2(x)
x = self.pool2(x)
x = self.conv3(x)
x = self.bn3(x, training=training)
x = self.leaky3(x)
x = self.pool3(x)
x = self.conv4(x)
x = self.bn4(x, training=training)
x = self.leaky4(x)
x = self.reshape(x)
features = self.fc(x)
return features
class BahdanauAttention(keras.layers.Layer):
def __init__(self, units):
super(BahdanauAttention, self).__init__()
self.W1 = layers.Dense(units, name='attention_W1')
self.W2 = layers.Dense(units, name='attention_W2')
self.V = layers.Dense(1, name='attention_V')
self.units = units
def call(self, features, hidden):
hidden_with_time_axis = tf.expand_dims(hidden, 1)
score = tf.nn.tanh(self.W1(features) + self.W2(hidden_with_time_axis))
attention_weights = self.V(score)
attention_weights = tf.nn.softmax(attention_weights, axis=1)
context_vector = attention_weights * features
context_vector = tf.reduce_sum(context_vector, axis=1)
attention_weights = tf.squeeze(attention_weights, axis=-1)
return context_vector, attention_weights
class RNN_Decoder(keras.Model):
def __init__(self, embedding_dim=256, units=512, vocab_size=10000, rnn_type='lstm'):
super(RNN_Decoder, self).__init__()
self.units = units
self.embedding_dim = embedding_dim
self.vocab_size = vocab_size
self.rnn_type = rnn_type.lower()
self.embedding = layers.Embedding(vocab_size, embedding_dim, name='word_embedding')
self.attention = BahdanauAttention(self.units)
if self.rnn_type == 'lstm':
self.rnn = layers.LSTM(
self.units,
return_sequences=True,
return_state=True,
recurrent_initializer='glorot_uniform',
name='lstm_decoder'
)
else:
self.rnn = layers.GRU(
self.units,
return_sequences=True,
return_state=True,
recurrent_initializer='glorot_uniform',
name='gru_decoder'
)
self.fc1 = layers.Dense(self.units, activation='relu', name='fc1')
self.fc2 = layers.Dense(vocab_size, name='fc2_output')
def call(self, x, features, hidden, carry=None, training=False):
context_vector, attention_weights = self.attention(features, hidden)
x = self.embedding(x)
context_vector_expanded = tf.expand_dims(context_vector, 1)
x = tf.concat([context_vector_expanded, x], axis=-1)
if self.rnn_type == 'lstm':
if carry is None:
carry = tf.zeros_like(hidden)
output, state_h, state_c = self.rnn(x, initial_state=[hidden, carry], training=training)
hidden = state_h
carry = state_c
else:
output, state = self.rnn(x, initial_state=hidden, training=training)
hidden = state
carry = None
x = self.fc1(output)
x = tf.reshape(x, (-1, x.shape[2]))
predictions = self.fc2(x)
return predictions, hidden, carry, attention_weights
def reset_state(self, batch_size):
hidden = tf.zeros((batch_size, self.units))
if self.rnn_type == 'lstm':
carry = tf.zeros((batch_size, self.units))
return [hidden, carry]
else:
return [hidden]
if __name__ == "__main__":
print("Testing Custom CNN Encoder-Decoder Model...")
EMBEDDING_DIM = 256
UNITS = 512
VOCAB_SIZE = 10000
BATCH_SIZE = 4
IMG_SIZE = 299
encoder = CNN_Encoder(embedding_dim=EMBEDDING_DIM)
print(f"\nEncoder created with embedding_dim={EMBEDDING_DIM}")
decoder = RNN_Decoder(
embedding_dim=EMBEDDING_DIM,
units=UNITS,
vocab_size=VOCAB_SIZE,
rnn_type='lstm'
)
print(f"Decoder created with units={UNITS}, vocab_size={VOCAB_SIZE}")
print("\nTesting forward pass...")
dummy_img = tf.random.normal((BATCH_SIZE, IMG_SIZE, IMG_SIZE, 3))
print(f"Input image shape: {dummy_img.shape}")
features = encoder(dummy_img, training=False)
print(f"Encoder output (features) shape: {features.shape}")
decoder_states = decoder.reset_state(batch_size=BATCH_SIZE)
hidden = decoder_states[0]
carry = decoder_states[1] if len(decoder_states) > 1 else None
print(f"Decoder hidden state shape: {hidden.shape}")
dummy_token = tf.constant([[1], [2], [3], [4]])
predictions, new_hidden, new_carry, attn_weights = decoder(
dummy_token, features, hidden, carry, training=False
)
print(f"Decoder predictions shape: {predictions.shape}")
print(f"Decoder attention weights shape: {attn_weights.shape}")
print(f"New hidden state shape: {new_hidden.shape}")
print("\n✅ Model test completed successfully!")
|