python-kerasHow do I use Python and Keras to train a model?

To train a model using Python and Keras, you will need to create a neural network. This neural network will require an input layer, an output layer, and at least one hidden layer. You can create these layers with the Keras API.

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

model = Sequential()
model.add(Dense(units=64, activation='relu', input_dim=100))
model.add(Dense(units=10, activation='softmax'))

Once you have created the model, you can compile it with the appropriate optimizer, loss function, and metrics.

model.compile(loss='categorical_crossentropy',
              optimizer='sgd',
              metrics=['accuracy'])

Finally, you can fit the model to your data using the fit method.

model.fit(x_train, y_train, epochs=5, batch_size=32)

The output of the above code will be the loss and accuracy of the model on the training data.

Code explanation

1. from keras.models import Sequential: imports the Sequential model from the Keras package.
2. from keras.layers import Dense: imports the Dense layer from the Keras package.
3. model = Sequential(): creates a new sequential model.
4. model.add(Dense(units=64, activation='relu', input_dim=100)): adds a new dense layer to the model with 64 neurons, a ReLU activation function, and an input dimension of 100.
5. model.add(Dense(units=10, activation='softmax')): adds a new dense layer to the model with 10 neurons and a Softmax activation function.
6. model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy']): compiles the model with the appropriate optimizer, loss function, and metrics.
7. model.fit(x_train, y_train, epochs=5, batch_size=32): fits the model to the training data for 5 epochs with a batch size of 32.

Helpful links

• Keras Model
• Keras Layers
• Keras Optimizers
• Keras Loss Functions

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