python-tensorflowHow do I use the model.fit method in Python Tensorflow?

The model.fit() method in Python TensorFlow is used to train a model. It takes in the input features and labels, and then fits the model to the data. To use the model.fit() method, you must first create a model and compile it. Then, you can call the model.fit() method and pass in the training data.

Example code

model = Sequential()
model.add(Dense(32, activation='relu', input_dim=784))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer='rmsprop',
              loss='categorical_crossentropy',
              metrics=['accuracy'])

# Train the model, iterating on the data in batches of 32 samples
model.fit(x_train, y_train, epochs=10, batch_size=32)

This code will train the model on the data x_train and y_train for 10 epochs, with a batch size of 32.

Parts of the code:

• model = Sequential() - This creates a new Sequential model.
• model.add(Dense(32, activation='relu', input_dim=784)) - This adds a fully-connected layer with 32 units and ReLU activation to the model.
• model.add(Dense(10, activation='softmax')) - This adds a fully-connected layer with 10 units and a softmax activation to the model.
• model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy']) - This compiles the model with the RMSprop optimizer, the categorical cross-entropy loss function, and accuracy as the metric.
• model.fit(x_train, y_train, epochs=10, batch_size=32) - This fits the model to the training data for 10 epochs, with a batch size of 32.

Helpful links

• TensorFlow Documentation - Model Training
• TensorFlow Documentation - Sequential Model

Edit this code on GitHub