Datasets:
HenryAI
/
KerasBERTv1-Data

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# Instantiate an optimizer.
optimizer = keras.optimizers.SGD(learning_rate=1e-3)
# Instantiate a loss function.
loss_fn = keras.losses.SparseCategoricalCrossentropy(from_logits=True)
# Prepare the training dataset.
batch_size = 64
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
x_train = np.reshape(x_train, (-1, 784))
x_test = np.reshape(x_test, (-1, 784))
train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))
train_dataset = train_dataset.shuffle(buffer_size=1024).batch(batch_size)
Here's our training loop:
We open a for loop that iterates over epochs
For each epoch, we open a for loop that iterates over the dataset, in batches
For each batch, we open a GradientTape() scope
Inside this scope, we call the model (forward pass) and compute the loss
Outside the scope, we retrieve the gradients of the weights of the model with regard to the loss
Finally, we use the optimizer to update the weights of the model based on the gradients
epochs = 2
for epoch in range(epochs):
print("\nStart of epoch %d" % (epoch,))
# Iterate over the batches of the dataset.
for step, (x_batch_train, y_batch_train) in enumerate(train_dataset):
# Open a GradientTape to record the operations run
# during the forward pass, which enables auto-differentiation.
with tf.GradientTape() as tape:
# Run the forward pass of the layer.
# The operations that the layer applies
# to its inputs are going to be recorded
# on the GradientTape.
logits = model(x_batch_train, training=True) # Logits for this minibatch
# Compute the loss value for this minibatch.
loss_value = loss_fn(y_batch_train, logits)
# Use the gradient tape to automatically retrieve
# the gradients of the trainable variables with respect to the loss.
grads = tape.gradient(loss_value, model.trainable_weights)
# Run one step of gradient descent by updating
# the value of the variables to minimize the loss.
optimizer.apply_gradients(zip(grads, model.trainable_weights))
# Log every 200 batches.
if step % 200 == 0:
print(
"Training loss (for one batch) at step %d: %.4f"
% (step, float(loss_value))
)
print("Seen so far: %s samples" % ((step + 1) * 64))
Start of epoch 0
Training loss (for one batch) at step 0: 76.3562
Seen so far: 64 samples
Training loss (for one batch) at step 200: 1.3921
Seen so far: 12864 samples
Training loss (for one batch) at step 400: 1.0018
Seen so far: 25664 samples
Training loss (for one batch) at step 600: 0.8904
Seen so far: 38464 samples
Training loss (for one batch) at step 800: 0.8393
Seen so far: 51264 samples
Start of epoch 1
Training loss (for one batch) at step 0: 0.8572
Seen so far: 64 samples
Training loss (for one batch) at step 200: 0.7616
Seen so far: 12864 samples
Training loss (for one batch) at step 400: 0.8453
Seen so far: 25664 samples
Training loss (for one batch) at step 600: 0.4959
Seen so far: 38464 samples
Training loss (for one batch) at step 800: 0.9363
Seen so far: 51264 samples
Low-level handling of metrics
Let's add metrics monitoring to this basic loop.
You can readily reuse the built-in metrics (or custom ones you wrote) in such training loops written from scratch. Here's the flow:
Instantiate the metric at the start of the loop
Call metric.update_state() after each batch
Call metric.result() when you need to display the current value of the metric
Call metric.reset_states() when you need to clear the state of the metric (typically at the end of an epoch)
Let's use this knowledge to compute SparseCategoricalAccuracy on validation data at the end of each epoch:
# Get model
inputs = keras.Input(shape=(784,), name="digits")
x = layers.Dense(64, activation="relu", name="dense_1")(inputs)
x = layers.Dense(64, activation="relu", name="dense_2")(x)
outputs = layers.Dense(10, name="predictions")(x)
model = keras.Model(inputs=inputs, outputs=outputs)
# Instantiate an optimizer to train the model.
optimizer = keras.optimizers.SGD(learning_rate=1e-3)
# Instantiate a loss function.
loss_fn = keras.losses.SparseCategoricalCrossentropy(from_logits=True)