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python-kerasHow can I visualize a Keras model using Python?

Visualizing a Keras model using Python is a powerful way to understand the inner workings of a model. To do this, you must first create a model with a summary() function. The summary() function will provide a visualization of the model's layers, weights, and connections.

Example code

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

model = Sequential()
model.add(Dense(units=64, activation='relu', input_dim=32))
model.add(Dense(units=2, activation='softmax'))


Output example

Model: "sequential_1"
Layer (type)                 Output Shape              Param #
dense_1 (Dense)              (None, 64)                2080
dense_2 (Dense)              (None, 2)                 130
Total params: 2,210
Trainable params: 2,210
Non-trainable params: 0

Code explanation

  1. from keras.models import Sequential - imports the Sequential model from the Keras library.
  2. from keras.layers import Dense - imports the Dense layer from the Keras library.
  3. model = Sequential() - creates a Sequential model object.
  4. model.add(Dense(units=64, activation='relu', input_dim=32)) - adds a Dense layer with 64 units, ReLU activation, and 32 input dimensions to the model.
  5. model.add(Dense(units=2, activation='softmax')) - adds a Dense layer with 2 units and softmax activation to the model.
  6. model.summary() - prints a summary of the model's layers, weights, and connections.

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