keras summary

Keras is an open-source neural network library written in Python. It is designed to enable fast experimentation with deep neural networks and easy deployment to production. Keras provides a high-level API for building and training deep learning models.

The Keras summary method is used to provide a summary of the model architecture, including the number of parameters and the output shape of each layer. This summary can be useful for debugging and optimizing the model, as well as understanding its structure and behavior.

The Keras summary method takes an optional argument called "line_length," which specifies the maximum length of each line in the summary output. If the line length is too short, the summary may be split across multiple lines, making it difficult to read. If the line length is too long, the summary may become too wide to fit on the screen.

To use the Keras summary method, first create a Keras model by defining its layers and compiling it with an optimizer and loss function. Then, call the summary method on the model object:

from keras.models import Sequential
from keras.layers import Dense

# Define a simple Keras model
model = Sequential()
model.add(Dense(64, activation='relu', input_dim=100))
model.add(Dense(1, activation='sigmoid'))

# Compile the model with an optimizer and loss function
model.compile(optimizer='rmsprop',
              loss='binary_crossentropy',
              metrics=['accuracy'])

# Print a summary of the model architecture
model.summary()

The output of the summary method will look something like this:

Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_1 (Dense)              (None, 64)                6464      
_________________________________________________________________
dense_2 (Dense)              (None, 1)                 65        
=================================================================
Total params: 6,529
Trainable params: 6,529
Non-trainable params: 0
_________________________________________________________________

This summary shows that the model has two layers, one with 64 neurons and one with 1 neuron, and a total of 6,529 parameters. It also shows the output shape of each layer, which is (None, 64) for the first layer and (None, 1) for the second layer. Finally, it shows the total number of trainable parameters and non-trainable parameters in the model.