Definition of Keras input
Keras input is part of the Tensorflow library which is mostly used for providing a Keras object related to tensor. This tensor object related to Keras can be used for augmenting certain attributes with objects for manipulation. These attributes are then used within the object for building and designing Keras model accordingly. In this model building continuous feedback with a set of input and set of output is required mandatorily. Keras input also deals with the layers of the model according to the requirement which makes designing and other fabrication of the model with proper fitting within it.
Keras input Explanation
Keras input is used for providing proper input with proper output.
- In Keras, the input layer plays a significant role as it gives some information to the model or the image.
- Each of the keras layers has some useful information that needs a proper channel for transferring information.
- In keras layers, there are certain units that represent each of the neurons in it and then each of the layers are associated with certain network topologies.
- There are tuples representing elements in an array or tensor flow in dimensions.
- Input shapes are the emphasizing channel for any of the input layer as it helps in providing proper data and shapes for input to make use of data training to compute layers automatically.
With shapes another important attribute for keras input is the keras dimensions associated with each layer which are as follows:
– Dense layers
– Shape for other tensors
– 2D convolution layer
– 1D convolution layer
These keras input to the layers can be anything like dimensions, weights, input shapes, units anything. The input layer is the starting layer or the initial layer from where the entire flow of data with its subsequent layers happens till the end layer.
Although the keras layer is the starting point or initiation of any flow it just takes the parameter but is not an actual layer to directly interact with another set of layers for making input parameters reach their actual point of contact includes a hidden layer definition in between where the connectivity between starting and connecting point happens in this way.
2D convolution or 1D convolution layer also makes some of the traversings in another way through the hidden layer consecutively.
Keras input Code
Since keras input refers to a combination of many layers and is the starting point of any layer flow and model training which can be either performed using Sequential API or by using functional API. It is always recommended to make use of the functional API() type for such implementation as it helps in defining functions with some customs whereas sequential API takes some complex parameters which become a little difficult to handle the flow or processing of the data at each layer. Both the ways considering sequential API and functional API with parameters are represented below:
Keras input code using Sequential API
Here a model is prepared using Sequential API where the input layer is fed in a way where sequential API has passed certain parameters with dimensions that can be customized accordingly. Since the input layer is not any layer it is just the set of input values that needs to be passed therefore it happens via hidden layer which means the input shape is provided with a value of 3 followed by the addition of another layer ending with an output layer. Output layer provides a value with a range of 1.
Keras input code using Functional API
Functional API is used for designing a model where the model gets in certain input using input tensor and then that input is provided with some of the hidden layers which comprise of other hidden layers with input to provide the respective output. Here the definition is made in a model with start and endpoint.
At least the model provides attributes with these parameters are provided to model.
Keras input shape
The input function is used to instantiate a Keras tensor which basically includes attributes that allow us to provide certain functionalities to the model. It is a tensor-type object that allows making instances with the model just by knowing its input and respective output.
For example, if the implementation requires input as [a,b] and output requires value as [c] then, in that case, it is required to make the inputs with a set of consecutive output.
Arguments involve the following parameters for any functioning which are as follows:
– Batch_size
– Input shape
– Name
– D_type
– Tensor
– Sparse
– Type_spec
– Ragged
– **kwargs
All these arguments come into the picture only after the input layer is defined with the shape input.
Input shape in Keras or input layer is the tuple which is a set of integers that don’t include any of the batch sizes. To get a clear understanding let’s take an example:
I am sending an input of shape (2×2440) which has 2 rows and 2440 columns which means that the column represents features for it. There are in total nearly 70000 vectors in it. But let’s suppose a query is made on it which gives some response as (?, 2440) then what actually it means is that the model is expecting an arbitrary number of 2×2440 vectors which generally don’t want to fix the number of inputs to the model of a certain number.
If the vector defined gives the value as none then in that case it is representing that the input shape provided is not considering the elements it means that it is empty and not able to detect any shape for the model to be designed for. Tuples for dimensionality play a major role in it.
Conclusion
Keras input plays a very pivotal role in designing and modeling as it involves enhanced data structure and Machine learning algorithm working in the background. Keras input consists of shapes and other attributes with respect to layers that further can provide errors in the values or arguments defined with the function.
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This is a guide to Keras input. Here we discuss the definition, explanation, shape, Keras input code using Sequential API, attributes with layers. You may also have a look at the following articles to learn more –
