HenryAI/KerasBERTv1-Data · Datasets at Hugging Face

text stringlengths 0 4.99k
0: 1.0,
1: 1.0,
2: 1.0,
3: 1.0,
4: 1.0,
# Set weight "2" for class "5",
# making this class 2x more important
5: 2.0,
6: 1.0,
7: 1.0,
8: 1.0,
9: 1.0,
}

print("Fit with class weight")
model = get_compiled_model()
model.fit(x_train, y_train, class_weight=class_weight, batch_size=64, epochs=1)
Fit with class weight
782/782 [==============================] - 1s 933us/step - loss: 0.6334 - sparse_categorical_accuracy: 0.8297

<tensorflow.python.keras.callbacks.History at 0x14e20f990>
Sample weights
For fine grained control, or if you are not building a classifier, you can use "sample weights".

When training from NumPy data: Pass the sample_weight argument to Model.fit().
When training from tf.data or any other sort of iterator: Yield (input_batch, label_batch, sample_weight_batch) tuples.
A "sample weights" array is an array of numbers that specify how much weight each sample in a batch should have in computing the total loss. It is commonly used in imbalanced classification problems (the idea being to give more weight to rarely-seen classes).

When the weights used are ones and zeros, the array can be used as a mask for the loss function (entirely discarding the contribution of certain samples to the total loss).

sample_weight = np.ones(shape=(len(y_train),))
sample_weight[y_train == 5] = 2.0

print("Fit with sample weight")
model = get_compiled_model()
model.fit(x_train, y_train, sample_weight=sample_weight, batch_size=64, epochs=1)
Fit with sample weight
782/782 [==============================] - 1s 899us/step - loss: 0.6337 - sparse_categorical_accuracy: 0.8355

<tensorflow.python.keras.callbacks.History at 0x14e3538d0>
Here's a matching Dataset example:

sample_weight = np.ones(shape=(len(y_train),))
sample_weight[y_train == 5] = 2.0

# Create a Dataset that includes sample weights
# (3rd element in the return tuple).
train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train, sample_weight))

# Shuffle and slice the dataset.
train_dataset = train_dataset.shuffle(buffer_size=1024).batch(64)

model = get_compiled_model()
model.fit(train_dataset, epochs=1)
782/782 [==============================] - 1s 1ms/step - loss: 0.6539 - sparse_categorical_accuracy: 0.8364

<tensorflow.python.keras.callbacks.History at 0x14e49c390>
Passing data to multi-input, multi-output models
In the previous examples, we were considering a model with a single input (a tensor of shape (764,)) and a single output (a prediction tensor of shape (10,)). But what about models that have multiple inputs or outputs?

Consider the following model, which has an image input of shape (32, 32, 3) (that's (height, width, channels)) and a time series input of shape (None, 10) (that's (timesteps, features)). Our model will have two outputs computed from the combination of these inputs: a "score" (of shape (1,)) and a probability distribution over five classes (of shape (5,)).

image_input = keras.Input(shape=(32, 32, 3), name="img_input")
timeseries_input = keras.Input(shape=(None, 10), name="ts_input")

x1 = layers.Conv2D(3, 3)(image_input)
x1 = layers.GlobalMaxPooling2D()(x1)

x2 = layers.Conv1D(3, 3)(timeseries_input)
x2 = layers.GlobalMaxPooling1D()(x2)

x = layers.concatenate([x1, x2])

score_output = layers.Dense(1, name="score_output")(x)
class_output = layers.Dense(5, name="class_output")(x)

model = keras.Model(
inputs=[image_input, timeseries_input], outputs=[score_output, class_output]
)
Let's plot this model, so you can clearly see what we're doing here (note that the shapes shown in the plot are batch shapes, rather than per-sample shapes).

keras.utils.plot_model(model, "multi_input_and_output_model.png", show_shapes=True)
png

At compilation time, we can specify different losses to different outputs, by passing the loss functions as a list:

model.compile(
optimizer=keras.optimizers.RMSprop(1e-3),
loss=[keras.losses.MeanSquaredError(), keras.losses.CategoricalCrossentropy()],
)
If we only passed a single loss function to the model, the same loss function would be applied to every output (which is not appropriate here).

Likewise for metrics:

model.compile(
optimizer=keras.optimizers.RMSprop(1e-3),
loss=[keras.losses.MeanSquaredError(), keras.losses.CategoricalCrossentropy()],
metrics=[
[
keras.metrics.MeanAbsolutePercentageError(),

0: 1.0,

1: 1.0,

2: 1.0,

3: 1.0,

4: 1.0,

# Set weight "2" for class "5",

# making this class 2x more important

5: 2.0,

6: 1.0,

7: 1.0,

8: 1.0,

9: 1.0,

}

print("Fit with class weight")

model = get_compiled_model()

model.fit(x_train, y_train, class_weight=class_weight, batch_size=64, epochs=1)

Fit with class weight

782/782 [==============================] - 1s 933us/step - loss: 0.6334 - sparse_categorical_accuracy: 0.8297

<tensorflow.python.keras.callbacks.History at 0x14e20f990>

Sample weights

For fine grained control, or if you are not building a classifier, you can use "sample weights".

When training from NumPy data: Pass the sample_weight argument to Model.fit().

When training from tf.data or any other sort of iterator: Yield (input_batch, label_batch, sample_weight_batch) tuples.

A "sample weights" array is an array of numbers that specify how much weight each sample in a batch should have in computing the total loss. It is commonly used in imbalanced classification problems (the idea being to give more weight to rarely-seen classes).

When the weights used are ones and zeros, the array can be used as a mask for the loss function (entirely discarding the contribution of certain samples to the total loss).

sample_weight = np.ones(shape=(len(y_train),))

sample_weight[y_train == 5] = 2.0

print("Fit with sample weight")

model = get_compiled_model()

model.fit(x_train, y_train, sample_weight=sample_weight, batch_size=64, epochs=1)

Fit with sample weight

782/782 [==============================] - 1s 899us/step - loss: 0.6337 - sparse_categorical_accuracy: 0.8355

<tensorflow.python.keras.callbacks.History at 0x14e3538d0>

Here's a matching Dataset example:

sample_weight = np.ones(shape=(len(y_train),))

sample_weight[y_train == 5] = 2.0

# Create a Dataset that includes sample weights

# (3rd element in the return tuple).

train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train, sample_weight))

# Shuffle and slice the dataset.

train_dataset = train_dataset.shuffle(buffer_size=1024).batch(64)

model = get_compiled_model()

model.fit(train_dataset, epochs=1)

782/782 [==============================] - 1s 1ms/step - loss: 0.6539 - sparse_categorical_accuracy: 0.8364

<tensorflow.python.keras.callbacks.History at 0x14e49c390>

Passing data to multi-input, multi-output models

In the previous examples, we were considering a model with a single input (a tensor of shape (764,)) and a single output (a prediction tensor of shape (10,)). But what about models that have multiple inputs or outputs?

Consider the following model, which has an image input of shape (32, 32, 3) (that's (height, width, channels)) and a time series input of shape (None, 10) (that's (timesteps, features)). Our model will have two outputs computed from the combination of these inputs: a "score" (of shape (1,)) and a probability distribution over five classes (of shape (5,)).

image_input = keras.Input(shape=(32, 32, 3), name="img_input")

timeseries_input = keras.Input(shape=(None, 10), name="ts_input")

x1 = layers.Conv2D(3, 3)(image_input)

x1 = layers.GlobalMaxPooling2D()(x1)

x2 = layers.Conv1D(3, 3)(timeseries_input)

x2 = layers.GlobalMaxPooling1D()(x2)

x = layers.concatenate([x1, x2])

score_output = layers.Dense(1, name="score_output")(x)

class_output = layers.Dense(5, name="class_output")(x)

model = keras.Model(

inputs=[image_input, timeseries_input], outputs=[score_output, class_output]

)

Let's plot this model, so you can clearly see what we're doing here (note that the shapes shown in the plot are batch shapes, rather than per-sample shapes).

keras.utils.plot_model(model, "multi_input_and_output_model.png", show_shapes=True)

png

At compilation time, we can specify different losses to different outputs, by passing the loss functions as a list:

model.compile(

optimizer=keras.optimizers.RMSprop(1e-3),

loss=[keras.losses.MeanSquaredError(), keras.losses.CategoricalCrossentropy()],

)

If we only passed a single loss function to the model, the same loss function would be applied to every output (which is not appropriate here).

Likewise for metrics:

model.compile(

optimizer=keras.optimizers.RMSprop(1e-3),

loss=[keras.losses.MeanSquaredError(), keras.losses.CategoricalCrossentropy()],

metrics=[

[

keras.metrics.MeanAbsolutePercentageError(),