Datasets:
HenryAI
/
KerasBERTv1-Data

Dataset card Data Studio Files Community
text
stringlengths
0
4.99k
def call(self, inputs):
x = self.dense1(inputs)
return self.dense2(x)
model = MyModel()
If you subclass Model, you can optionally have a training argument (boolean) in call, which you can use to specify a different behavior in training and inference:
import tensorflow as tf
class MyModel(tf.keras.Model):
def __init__(self):
super(MyModel, self).__init__()
self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu)
self.dense2 = tf.keras.layers.Dense(5, activation=tf.nn.softmax)
self.dropout = tf.keras.layers.Dropout(0.5)
def call(self, inputs, training=False):
x = self.dense1(inputs)
if training:
x = self.dropout(x, training=training)
return self.dense2(x)
model = MyModel()
Once the model is created, you can config the model with losses and metrics with model.compile(), train the model with model.fit(), or use the model to do prediction with model.predict().
summary method
Model.summary(line_length=None, positions=None, print_fn=None)
Prints a string summary of the network.
Arguments
line_length: Total length of printed lines (e.g. set this to adapt the display to different terminal window sizes).
positions: Relative or absolute positions of log elements in each line. If not provided, defaults to [.33, .55, .67, 1.].
print_fn: Print function to use. Defaults to print. It will be called on each line of the summary. You can set it to a custom function in order to capture the string summary.
Raises
ValueError: if summary() is called before the model is built.
get_layer method
Model.get_layer(name=None, index=None)
Retrieves a layer based on either its name (unique) or index.
If name and index are both provided, index will take precedence. Indices are based on order of horizontal graph traversal (bottom-up).
Arguments
name: String, name of layer.
index: Integer, index of layer.
Returns
A layer instance.
Raises
ValueError: In case of invalid layer name or index.The Sequential class
Sequential class
tf.keras.Sequential(layers=None, name=None)
Sequential groups a linear stack of layers into a tf.keras.Model.
Sequential provides training and inference features on this model.
Examples
>>> # Optionally, the first layer can receive an `input_shape` argument:
>>> model = tf.keras.Sequential()
>>> model.add(tf.keras.layers.Dense(8, input_shape=(16,)))
>>> # Afterwards, we do automatic shape inference:
>>> model.add(tf.keras.layers.Dense(4))
>>> # This is identical to the following:
>>> model = tf.keras.Sequential()
>>> model.add(tf.keras.Input(shape=(16,)))
>>> model.add(tf.keras.layers.Dense(8))
>>> # Note that you can also omit the `input_shape` argument.
>>> # In that case the model doesn't have any weights until the first call
>>> # to a training/evaluation method (since it isn't yet built):
>>> model = tf.keras.Sequential()
>>> model.add(tf.keras.layers.Dense(8))
>>> model.add(tf.keras.layers.Dense(4))
>>> # model.weights not created yet
>>> # Whereas if you specify the input shape, the model gets built
>>> # continuously as you are adding layers:
>>> model = tf.keras.Sequential()
>>> model.add(tf.keras.layers.Dense(8, input_shape=(16,)))
>>> model.add(tf.keras.layers.Dense(4))
>>> len(model.weights)
4
>>> # When using the delayed-build pattern (no input shape specified), you can
>>> # choose to manually build your model by calling
>>> # `build(batch_input_shape)`:
>>> model = tf.keras.Sequential()
>>> model.add(tf.keras.layers.Dense(8))