We recently switched to Keras for TF 2.0, but when we compared it to the DNNClassifier Estimator on 2.0, we experienced around 4x slower speeds with Keras. But I cannot for the life of me figure out why this is happening. The rest of the code for both are identical, using an input_fn() that returns the same tf.data.Dataset, and using identical feature_columns. Been struggling with this problem for days now. Any help would be greatly greatly appreciated. Thank you
Estimator code:
estimator = tf.estimator.DNNClassifier(
feature_columns = feature_columns,
hidden_units = [64,64],
activation_fn = tf.nn.relu,
optimizer = 'Adagrad',
dropout = 0.4,
n_classes = len(vocab),
model_dir = model_dir,
batch_norm = false)
estimator.train(input_fn=train_input_fn, steps=400)
Keras code:
feature_layer = tf.keras.layers.DenseFeatures(feature_columns);
model = tf.keras.Sequential([
feature_layer,
layers.Dense(64, input_shape = (len(vocab),), activation = tf.nn.relu),
layers.Dropout(0.4),
layers.Dense(64, activation = tf.nn.relu),
layers.Dropout(0.4),
layers.Dense(len(vocab), activation = 'softmax')]);
model.compile(
loss = 'sparse_categorical_crossentropy',
optimizer = 'Adagrad'
distribute = None)
model.fit(x = train_input_fn(),
epochs = 1,
steps_per_epoch = 400,
shuffle = True)
UPDATE: To test further, I wrote a custom subclassed Model (See: Get Started For Experts), which runs faster than Keras but slower than Estimators. If Estimator trains in 100 secs, the custom model takes approx ~180secs, and Keras approx ~350secs. An interesting note is that Estimator runs slower with Adam() than Adagrad() while Keras seems to run faster. With Adam() Keras takes less than twice as long as DNNClassifier. Assuming I didn't mess up the custom code, I'm beginning to think that DNNClassifier just has a lot of backend optimization / efficiencies that make it run faster than Keras.
Custom code:
class MyModel(Model):
def __init__(self):
super(MyModel, self).__init__()
self.features = layers.DenseFeatures(feature_columns, trainable=False)
self.dense = layers.Dense(64, activation = 'relu')
self.dropout = layers.Dropout(0.4)
self.dense2 = layers.Dense(64, activation = 'relu')
self.dropout2 = layers.Dropout(0.4)
self.softmax = layers.Dense(len(vocab_of_codes), activation = 'softmax')
def call(self, x):
x = self.features(x)
x = self.dense(x)
x = self.dropout(x)
x = self.dense2(x)
x = self.dropout2(x)
return self.softmax(x)
model = MyModel()
loss_object = tf.keras.losses.SparseCategoricalCrossentropy()
optimizer = tf.keras.optimizers.Adagrad()
@tf.function
def train_step(features, label):
with tf.GradientTape() as tape:
predictions = model(features)
loss = loss_object(label, predictions)
gradients = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
itera = iter(train_input_fn())
for i in range(400):
features, labels = next(itera)
train_step(features, labels)
UPDATE: It possibly seems to be the dataset. When I print a row of the dataset within the train_input_fn(), in estimators, it prints out the non-eager Tensor definition. In Keras, it prints out the eager values. Going through the Keras backend code, when it receives a tf.data.dataset as input, it handles it eagerly (and ONLY eagerly), which is why it was crashing whenever I used tf.function on the train_input_fn(). Basically, my guess is DNNClassifier is training faster than Keras because it runs more dataset code in graph mode. Will post any updates/finds.
