Metadata-Version: 2.1
Name: keract
Version: 1.1.2
Summary: Keras Activations
Home-page: UNKNOWN
Author: Philippe Remy
License: MIT
Description: # Keras Activations
        ```
        pip install keract
        ```
        *You have just found a (easy) way to get the activations for each layer of your Keras model (recurrent nets, conv nets, resnets...).*
        
        <p align="center">
          <img src="assets/1.png">
        </p>
        
        
        ## API
        
        ```
        from keract import get_activations
        get_activations(model, x)
        ```
        
        ### Inputs
        - `model` is a `keras.models.Model` object
        - `x` is a numpy array to feed to the model as input. In the case of multi-input, `x` is of type List. We use the Keras convention (as used in predict, fit...).
        
        ### Output
        - A dictionary containing the activations for each layer of `model` for the input `x`:
        
        ```
        {
          'conv2d_1/Relu:0': np.array(...),
          'conv2d_2/Relu:0': np.array(...),
          ...,
          'dense_2/Softmax:0': np.array(...)
        }
        ```
        
        The key is the name of the layer and the value is the corresponding output of the layer for the given input `x`.
        
        ## Examples
        
        Examples are provided for:
        - `keras.models.Sequential` - mnist.py
        - `keras.models.Model` - multi_inputs.py
        - Recurrent networks - recurrent.py
        
        In the case of MNIST with LeNet, we are able to fetch the activations for a batch of size 128:
        
        ```
        conv2d_1/Relu:0
        (128, 26, 26, 32)
        
        conv2d_2/Relu:0
        (128, 24, 24, 64)
        
        max_pooling2d_1/MaxPool:0
        (128, 12, 12, 64)
        
        dropout_1/cond/Merge:0
        (128, 12, 12, 64)
        
        flatten_1/Reshape:0
        (128, 9216)
        
        dense_1/Relu:0
        (128, 128)
        
        dropout_2/cond/Merge:0
        (128, 128)
        
        dense_2/Softmax:0
        (128, 10)
        ```
        
        We can even visualise some of them.
        
        <p align="center">
          <img src="assets/0.png" width="50">
          <br><i>A random seven from MNIST</i>
        </p>
        
        
        <p align="center">
          <img src="assets/1.png">
          <br><i>Activation map of CONV1 of LeNet</i>
        </p>
        
        <p align="center">
          <img src="assets/2.png" width="200">
          <br><i>Activation map of FC1 of LeNet</i>
        </p>
        
        
        <p align="center">
          <img src="assets/3.png" width="300">
          <br><i>Activation map of Softmax of LeNet. <b>Yes it's a seven!</b></i>
        </p>
        
        
Platform: UNKNOWN
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