pip install keract

You have just found a (easy) way to get the activations (outputs) and gradients for each layer of your Keras model (LSTM, conv nets...).

• get_activations
• display_activations
• display_heatmaps
• get_gradients_of_trainable_weights
• get_gradients_of_activations
• persist_to_json_file
• load_activations_from_json_file

keract.get_activations(model, x, layer_names=None, nodes_to_evaluate=None, output_format='simple', auto_compile=True)

Fetch activations (nodes/layers outputs as Numpy arrays) for a Keras model and an input X. By default, all the activations for all the layers are returned.

• model: Keras compiled model or one of ['vgg16', 'vgg19', 'inception_v3', 'inception_resnet_v2', 'mobilenet_v2', 'mobilenetv2', ...].
• x: Numpy array to feed the model as input. In the case of multi-inputs, x should be of type List.
• layer_names: (optional) Single name of a layer or list of layer names for which activations should be returned. It is useful in very big networks when it is computationally expensive to evaluate all the layers/nodes.
• nodes_to_evaluate: (optional) List of Keras nodes to be evaluated.
• output_format: Change the output dictionary key of the function.
  • simple: output key will match the names of the Keras layers. For example Dense(1, name='d1') will return {'d1': ...}.
  • full: output key will match the full name of the output layer name. In the example above, it will return {'d1/BiasAdd:0': ...}.
  • numbered: output key will be an index range, based on the order of definition of each layer within the model.
• auto_compile: If set to True, will auto-compile the model if needed.

Returns: Dict {layer_name (specified by output_format) -> activation of the layer output/node (Numpy array)}.

Example

import numpy as np
from tensorflow.keras import Input, Model
from tensorflow.keras.layers import Dense, concatenate
from keract import get_activations

# model definition
i1 = Input(shape=(10,), name='i1')
i2 = Input(shape=(10,), name='i2')

a = Dense(1, name='fc1')(i1)
b = Dense(1, name='fc2')(i2)

c = concatenate([a, b], name='concat')
d = Dense(1, name='out')(c)
model = Model(inputs=[i1, i2], outputs=[d])

# inputs to the model
x = [np.random.uniform(size=(32, 10)), np.random.uniform(size=(32, 10))]

# call to fetch the activations of the model.
activations = get_activations(model, x, auto_compile=True)

# print the activations shapes.
[print(k, '->', v.shape, '- Numpy array') for (k, v) in activations.items()]

# Print output:
# i1 -> (32, 10) - Numpy array
# i2 -> (32, 10) - Numpy array
# fc1 -> (32, 1) - Numpy array
# fc2 -> (32, 1) - Numpy array
# concat -> (32, 2) - Numpy array
# out -> (32, 1) - Numpy array

keract.display_activations(activations, cmap=None, save=False, directory='.', data_format='channels_last')

Plot the activations for each layer using matplotlib

Inputs are:

• activations dict - a dictionary mapping layers to their activations (the output of get_activations)
• cmap (optional) string - a valid matplotlib colormap to be used
• save(optional) a bool, if True the images of the activations are saved rather than being shown
• directory: (optional) string - where to store the activations (if save is True)
• data_format: (optional) tring - one of "channels_last" (default) or "channels_first".

The ordering of the dimensions in the inputs. "channels_last" corresponds to inputs with shape (batch, steps, channels) (default format for temporal data in Keras) while "channels_first" corresponds to inputs with shape (batch, channels, steps).

keract.display_heatmaps(activations, input_image, save=False)

Plot heatmaps of activations for all filters overlayed on the input image for each layer

Inputs are:

• activations: a dictionary mapping layers to their activations (the output of get_activations).
• input_image: numpy array of the image you inputed to the get_activations.
• save(optional) bool - if True the images of the activations are saved rather than being shown.
• fix: (optional) bool - if automated checks and fixes for incorrect images should be run.
• directory: string - where to store the activations (if save is True).

keract.get_gradients_of_trainable_weights(model, x, y)

• model is a keras.models.Model object.
• x: Numpy array to feed the model as input. In the case of multi-inputs, x should be of type List.
• y: Labels (numpy array). Keras convention.

The output is a dictionary mapping each trainable weight to the values of its gradients (regarding x and y).

keract.get_gradients_of_activations(model, x, y, layer_name=None, output_format='simple')

• model is a keras.models.Model object.
• x: Numpy array to feed the model as input. In the case of multi-inputs, x should be of type List.
• y: Labels (numpy array). Keras convention.
• layer_name: (optional) Name of a layer for which activations should be returned.
• output_format: Change the output dictionary key of the function.
  • simple: output key will match the names of the Keras layers. For example Dense(1, name='d1') will return {'d1': ...}.
  • full: output key will match the full name of the output layer name. In the example above, it will return {'d1/BiasAdd:0': ...}.
  • numbered: output key will be an index range, based on the order of definition of each layer within the model.

Returns: Dict {layer_name (specified by output_format) -> grad activation of the layer output/node (Numpy array)}.

The output is a dictionary mapping each layer to the values of its gradients (regarding x and y).

keract.persist_to_json_file(activations, filename)

• activations: activations (dict mapping layers)
• filename: output filename (JSON format)

keract.load_activations_from_json_file(filename)

• filename: filename to read the activations from (JSON format)

It returns the activations.

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 visualise the activations. Here's another example using VGG16:

cd examples
pip install -r examples-requirements.txt
python vgg16.py

A cat.

Outputs of the first convolutional layer of VGG16.

Also, we can visualise the heatmaps of the activations:

cd examples
pip install -r examples-requirements.txt
python heat_map.py