Mẹo về Conv3d là gì 2022
Ban đang tìm kiếm từ khóa Conv3d là gì được Update vào lúc : 2022-02-16 10:26:16 . Với phương châm chia sẻ Kinh Nghiệm Hướng dẫn trong nội dung bài viết một cách Chi Tiết 2022. Nếu sau khi tìm hiểu thêm tài liệu vẫn ko hiểu thì hoàn toàn có thể lại Comments ở cuối bài để Admin lý giải và hướng dẫn lại nha.
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- TensorFlowAPITensorFlow Core v2.8.0Python
tf.keras.layers.Dense
Just your regular densely-connected NN layer.
Nội dung chính
- tf.keras.layers.DenseUsed in the notebooksVideo liên quan
Inherits From: Layer, ModuleView aliases
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.keras.layers.Dense
tf.keras.layers.Dense(
units, activation=None, use_bias=True,
kernel_initializer=”glorot_uniform”,
bias_initializer=”zeros”, kernel_regularizer=None,
bias_regularizer=None, activity_regularizer=None, kernel_constraint=None,
bias_constraint=None, **kwargs
)
Used in the notebooks
Used in the guideUsed in the tutorials
- Save and load Keras modelsThe Sequential modelTraining and evaluation with the built-in methodsThe Functional APIMaking new Layers and Models via subclassing
- Overfit and underfitTime series forecastingLoad a pandas DataFrameIntro to AutoencodersLoad CSV data
Dense implements the operation: output = activation(dot(input, kernel) + bias) where activation is the element-wise activation function passed as the activation argument, kernel is a weights matrix created by the layer, and bias is a bias vector created by the layer (only applicable if use_bias is True). These are all attributes of Dense.
Note: If the input to the layer has a rank greater than 2, then Dense computes the dot product between the inputs and the kernel along the last axis of the inputs and axis 0 of the kernel (using tf.tensordot). For example, if input has dimensions (batch_size, d0, d1), then we create a kernel with shape (d1, units), and the kernel operates along axis 2 of the input, on every sub-tensor of shape (1, 1, d1) (there are batch_size * d0 such sub-tensors). The output in this case will have shape (batch_size, d0, units).
Besides, layer attributes cannot be modified after the layer has been called once (except the trainable attribute). When a popular kwarg input_shape is passed, then keras will create an input layer to insert before the current layer. This can be treated equivalent to explicitly defining an InputLayer.
Example:# Create a `Sequential` model and add a Dense layer as the first layer.
model = tf.keras.models.Sequential()
model.add(tf.keras.Input(shape=(16,)))
model.add(tf.keras.layers.Dense(32, activation=’relu’))
# Now the model will take as input arrays of shape (None, 16)
# and output arrays of shape (None, 32).
# Note that after the first layer, you don’t need to specify
# the size of the input anymore:
model.add(tf.keras.layers.Dense(32))
model.output_shape
(None, 32)
Args
unitsPositive integer, dimensionality of the output space.activationActivation function to use. If you don’t specify anything, no activation is applied (ie. “linear” activation: a(x) = x).use_biasBoolean, whether the layer uses a bias vector.kernel_initializerInitializer for the kernel weights matrix.bias_initializerInitializer for the bias vector.kernel_regularizerRegularizer function applied to the kernel weights matrix.bias_regularizerRegularizer function applied to the bias vector.activity_regularizerRegularizer function applied to the output of the layer (its “activation”).kernel_constraintConstraint function applied to the kernel weights matrix.bias_constraintConstraint function applied to the bias vector.Input shape:
N-D tensor with shape: (batch_size, …, input_dim). The most common situation would be a 2D input with shape (batch_size, input_dim).
Output shape:
N-D tensor with shape: (batch_size, …, units). For instance, for a 2D input with shape (batch_size, input_dim), the output would have shape (batch_size, units).
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