问题
I am currently trying to get a hold of the TF2.0 api, but as I compared the GradientTape to a regular keras.Model.fit I noticed:
It ran slower(probably due to the Eager Execution)
It converged much slower (and I am not sure why).
+--------+--------------+--------------+------------------+
| Epoch | GradientTape | GradientTape | keras.Model.fit |
| | | shuffling | |
+--------+--------------+--------------+------------------+
| 1 | 0.905 | 0.918 | 0.8793 |
+--------+--------------+--------------+------------------+
| 2 | 0.352 | 0.634 | 0.2226 |
+--------+--------------+--------------+------------------+
| 3 | 0.285 | 0.518 | 0.1192 |
+--------+--------------+--------------+------------------+
| 4 | 0.282 | 0.458 | 0.1029 |
+--------+--------------+--------------+------------------+
| 5 | 0.275 | 0.421 | 0.0940 |
+--------+--------------+--------------+------------------+
Here is the training loop I used with the GradientTape:
optimizer = keras.optimizers.Adam()
glove_model = GloveModel(vocab_size=len(labels))
train_loss = keras.metrics.Mean(name='train_loss')
@tf.function
def train_step(examples, labels):
with tf.GradientTape() as tape:
predictions = glove_model(examples)
loss = glove_model.glove_loss(labels, predictions)
gradients = tape.gradient(loss, glove_model.trainable_variables)
optimizer.apply_gradients(zip(gradients, glove_model.trainable_variables))
train_loss(loss)
total_step = 0
for epoch in range(epochs_number):
pbar = tqdm(train_ds.enumerate(), total=int(len(index_data) / batch_size) + 1)
for ix, (examples, labels) in pbar:
train_step(examples, labels)
print(f"Epoch {epoch + 1}, Loss {train_loss.result()}")
# Reset the metrics for the next epoch
train_loss.reset_states()
And here is the Keras.Model.fit training:
glove_model.compile(optimizer, glove_model.glove_loss)
glove_model.fit(train_ds, epochs=epochs_number)
Here is the tf.data.Dataset source
train_ds = data.Dataset.from_tensor_slices(
(np.hstack([index_rows.reshape(-1, 1), index_cols.reshape(-1, 1)]), index_data)
).shuffle(100000).batch(batch_size, drop_remainder=True)
And Here is the model.
class GloveModel(keras.Model):
def __init__(self, vocab_size, dim=100, a=3/4, x_max=100):
super(GloveModel, self).__init__()
self.vocab_size = vocab_size
self.dim = dim
self.a = a
self.x_max = x_max
self.target_embedding = layers.Embedding(
input_dim=self.vocab_size, output_dim=self.dim, input_length=1, name="target_embedding"
)
self.target_bias = layers.Embedding(
input_dim=self.vocab_size, output_dim=1, input_length=1, name="target_bias"
)
self.context_embedding = layers.Embedding(
input_dim=self.vocab_size, output_dim=self.dim, input_length=1, name="context_embedding"
)
self.context_bias = layers.Embedding(
input_dim=self.vocab_size, output_dim=1, input_length=1, name="context_bias"
)
self.dot_product = layers.Dot(axes=-1, name="dot")
self.prediction = layers.Add(name="add")
self.step = 0
def call(self, inputs):
target_ix = inputs[:, 0]
context_ix = inputs[:, 1]
target_embedding = self.target_embedding(target_ix)
target_bias = self.target_bias(target_ix)
context_embedding = self.context_embedding(context_ix)
context_bias = self.context_bias(context_ix)
dot_product = self.dot_product([target_embedding, context_embedding])
prediction = self.prediction([dot_product, target_bias, context_bias])
return prediction
def glove_loss(self, y_true, y_pred):
weight = tf.math.minimum(
tf.math.pow(y_true/self.x_max, self.a), 1.0
)
loss_value = tf.math.reduce_mean(weight * tf.math.pow(y_pred - tf.math.log(y_true), 2.0))
return loss_value
I tried multiple configurations and optimizers but nothing seems to change the convergence rate.
回答1:
Dataset.shuffle() only shuffle each minibatch, so each epoch has the same order. Keras .fit() uses some magics to shuffle the whole dataset before each epoch. To do this in TF, you need to use Dataset .repeat(epochs_number) and .shuffle(..., reshuffle_each_iteration=True):
train_ds = data.Dataset.from_tensor_slices(
(np.hstack([index_rows.reshape(-1, 1), index_cols.reshape(-1, 1)]), index_data)
).shuffle(100000, reshuffle_each_iteration=True
).batch(batch_size, drop_remainder=True
).repeat(epochs_number)
for ix, (examples, labels) in train_ds.enumerate():
train_step(examples, labels)
current_epoch = ix // (len(index_data) // batch_size)
This workaround is not beautiful nor natural, for the moment you can use this to shuffle each epoch. It's a known issue and will be fixed, in the future you can use for epoch in range(epochs_number) instead of .repeat().
回答2:
The problem came from the shuffling using the tf.Dataset method. It only shuffled through the dataset one bucket at the time. Using the Keras.Model.fit yielded better results because it probably adds another shuffling.
I added a shuffling with numpy.random.shuffle and it improved the performance with both training methods:
The generation of the dataset is now:
numpy_data = np.hstack([index_rows.reshape(-1, 1), index_cols.reshape(-1, 1), index_data.reshape(-1, 1)])
np.random.shuffle(numpy_data)
indexes = np.array(numpy_data[:, :2], dtype=np.uint32)
labels = np.array(numpy_data[:, 2].reshape(-1, 1), dtype=np.float32)
train_ds = data.Dataset.from_tensor_slices(
(indexes, labels)
).shuffle(100000).batch(batch_size, drop_remainder=True)
And the results are:
+--------+--------------+------------------+
| Epoch | GradientTape | keras.Model.fit |
+--------+--------------+------------------+
| 1 | 0.294 | 0.294 |
+--------+--------------+------------------+
| 2 | 0.111 | 0.110 |
+--------+--------------+------------------+
| 3 | 0.089 | 0.089 |
+--------+--------------+------------------+
| 4 | 0.074 | 0.075 |
+--------+--------------+------------------+
| 5 | 0.063 | 0.063 |
+--------+--------------+------------------+
The training type per epoch is roughly the same at 2minutes per epoch.
来源：https://stackoverflow.com/questions/58584359/gradientape-convergence-much-slower-than-keras-model-fit