File size: 4,922 Bytes
5d73fef |
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 |
import argparse
import tensorflow as tf
import model
from dataset import get_dataset, preprocess_sentence
class CustomSchedule(tf.keras.optimizers.schedules.LearningRateSchedule):
def __init__(self, d_model: int, warmup_steps: int = 4000):
super(CustomSchedule, self).__init__()
self.d_model = tf.cast(d_model, dtype=tf.float32)
self.warmup_steps = warmup_steps
def __call__(self, step):
arg1 = tf.math.rsqrt(step)
arg2 = step * self.warmup_steps**-1.5
return tf.math.rsqrt(self.d_model) * tf.math.minimum(arg1, arg2)
def inference(hparams, chatbot, tokenizer, sentence):
sentence = preprocess_sentence(sentence)
sentence = tf.expand_dims(
hparams.start_token + tokenizer.encode(sentence) + hparams.end_token, axis=0
)
output = tf.expand_dims(hparams.start_token, 0)
for _ in range(hparams.max_length):
predictions = chatbot(inputs=[sentence, output], training=False)
predictions = predictions[:, -1:, :]
predicted_id = tf.cast(tf.argmax(predictions, axis=-1), tf.int32)
if tf.equal(predicted_id, hparams.end_token[0]):
break
output = tf.concat([output, predicted_id], axis=-1)
return tf.squeeze(output, axis=0)
def predict(hparams, chatbot, tokenizer, sentence):
prediction = inference(hparams, chatbot, tokenizer, sentence)
predicted_sentence = tokenizer.decode(
[i for i in prediction if i < tokenizer.vocab_size]
)
return predicted_sentence
def evaluate(hparams, chatbot, tokenizer):
print("\nDeğerlendir")
sentence = "Merhaba nasılsın?"
output = predict(hparams, chatbot, tokenizer, sentence)
print(f"input: {sentence}\noutput: {output}")
sentence = "Sence de gökyüzü çok güzel değil mi?"
output = predict(hparams, chatbot, tokenizer, sentence)
print(f"\ninput: {sentence}\noutput: {output}")
sentence = "Sanırım uzaklara gideceğim."
for _ in range(5):
output = predict(hparams, chatbot, tokenizer, sentence)
print(f"\ninput: {sentence}\noutput: {output}")
sentence = output
def main(hparams):
tf.keras.utils.set_random_seed(1234)
data, token = get_dataset(hparams)
chatbot = model.transformer(hparams)
optimizer = tf.keras.optimizers.Adam(
CustomSchedule(d_model=hparams.d_model), beta_1=0.9, beta_2=0.98, epsilon=1e-9
)
cross_entropy = tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=True, reduction="none"
)
def loss_function(y_true, y_pred):
y_true = tf.reshape(y_true, shape=(-1, hparams.max_length - 1))
loss = cross_entropy(y_true, y_pred)
mask = tf.cast(tf.not_equal(y_true, 0), dtype=tf.float32)
loss = tf.multiply(loss, mask)
return tf.reduce_mean(loss)
def accuracy(y_true, y_pred):
y_true = tf.reshape(y_true, shape=(-1, hparams.max_length - 1))
return tf.keras.metrics.sparse_categorical_accuracy(y_true, y_pred)
chatbot.compile(optimizer, loss=loss_function, metrics=[accuracy])
chatbot.fit(data, epochs=hparams.epochs)
print(f"\nmodel {hparams.save_model}'a kaydediliyor...")
tf.keras.models.save_model(
chatbot, filepath=hparams.save_model, include_optimizer=False
)
print(
f"\nclear TensorFlow backend session and load model f rom {hparams.save_model}..."
)
del chatbot
tf.keras.backend.clear_session()
chatbot = tf.keras.models.load_model(
hparams.save_model,
custom_objects={
"PositionalEncoding": model.PositionalEncoding,
"MultiHeadAttention": model.MultiHeadAttention,
},
compile=False,
)
evaluate(hparams, chatbot, token)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--save_model", default="model.h5", type=str, help="path save the model"
)
parser.add_argument(
"--max_samples",
default=25000,
type=int,
help="maximum number of conversation pairs to use",
)
parser.add_argument(
"--max_length", default=40, type=int, help="maximum sentence length"
)
parser.add_argument("--batch_size", default=128, type=int)
parser.add_argument("--num_layers", default=2, type=int)
parser.add_argument("--num_units", default=512, type=int)
parser.add_argument("--d_model", default=512, type=int)
parser.add_argument("--num_heads", default=8, type=int)
parser.add_argument("--dropout", default=0.1, type=float)
parser.add_argument("--activation", default="relu", type=str)
parser.add_argument("--epochs", default=70, type=int)
main(parser.parse_args())
|