Datasets:
HenryAI
/
KerasCodeExamples.txt

Dataset card Data Studio Files
xet
 Community
text
stringlengths
0
4.99k
test_ds = prepare(test_ds)
Define a model
In this section we will define a Convolutional neural network.
model = tf.keras.Sequential(
[
layers.Conv2D(16, (3, 3), activation=\"relu\", input_shape=(300, 300, 3)),
layers.MaxPooling2D(2, 2),
layers.Conv2D(32, (3, 3), activation=\"relu\"),
layers.Dropout(0.5),
layers.MaxPooling2D(2, 2),
layers.Conv2D(64, (3, 3), activation=\"relu\"),
layers.Dropout(0.5),
layers.MaxPooling2D(2, 2),
layers.Conv2D(64, (3, 3), activation=\"relu\"),
layers.MaxPooling2D(2, 2),
layers.Conv2D(64, (3, 3), activation=\"relu\"),
layers.MaxPooling2D(2, 2),
layers.Flatten(),
layers.Dropout(0.5),
layers.Dense(512, activation=\"relu\"),
layers.Dense(1, activation=\"sigmoid\"),
]
)
Implement Gradient Centralization
We will now subclass the RMSProp optimizer class modifying the tf.keras.optimizers.Optimizer.get_gradients() method where we now implement Gradient Centralization. On a high level the idea is that let us say we obtain our gradients through back propogation for a Dense or Convolution layer we then compute the mean of th...
The experiments in this paper on various applications, including general image classification, fine-grained image classification, detection and segmentation and Person ReID demonstrate that GC can consistently improve the performance of DNN learning.
Also, for simplicity at the moment we are not implementing gradient cliiping functionality, however this quite easy to implement.
At the moment we are just creating a subclass for the RMSProp optimizer however you could easily reproduce this for any other optimizer or on a custom optimizer in the same way. We will be using this class in the later section when we train a model with Gradient Centralization.
class GCRMSprop(RMSprop):
def get_gradients(self, loss, params):
# We here just provide a modified get_gradients() function since we are
# trying to just compute the centralized gradients.
grads = []
gradients = super().get_gradients()
for grad in gradients:
grad_len = len(grad.shape)
if grad_len > 1:
axis = list(range(grad_len - 1))
grad -= tf.reduce_mean(grad, axis=axis, keep_dims=True)
grads.append(grad)
return grads
optimizer = GCRMSprop(learning_rate=1e-4)
Training utilities
We will also create a callback which allows us to easily measure the total training time and the time taken for each epoch since we are interested in comparing the effect of Gradient Centralization on the model we built above.
class TimeHistory(tf.keras.callbacks.Callback):
def on_train_begin(self, logs={}):
self.times = []
def on_epoch_begin(self, batch, logs={}):
self.epoch_time_start = time()
def on_epoch_end(self, batch, logs={}):
self.times.append(time() - self.epoch_time_start)
Train the model without GC
We now train the model we built earlier without Gradient Centralization which we can compare to the training performance of the model trained with Gradient Centralization.
time_callback_no_gc = TimeHistory()
model.compile(
loss=\"binary_crossentropy\",
optimizer=RMSprop(learning_rate=1e-4),
metrics=[\"accuracy\"],
)
model.summary()
Model: \"sequential_1\"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 298, 298, 16) 448
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 149, 149, 16) 0
_________________________________________________________________
conv2d_1 (Conv2D) (None, 147, 147, 32) 4640
_________________________________________________________________
dropout (Dropout) (None, 147, 147, 32) 0
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 73, 73, 32) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 71, 71, 64) 18496
_________________________________________________________________
dropout_1 (Dropout) (None, 71, 71, 64) 0
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 35, 35, 64) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 33, 33, 64) 36928
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 16, 16, 64) 0
_________________________________________________________________
conv2d_4 (Conv2D) (None, 14, 14, 64) 36928
_________________________________________________________________