List of callbacks to apply during training. Verbosity mode.Ġ = silent, 1 = progress bar, 2 = one line per epoch. Given by epochs, but merely until the epoch

The model is not trained for a number of iterations Note that in conjunction with initial_epoch,Įpochs is to be understood as 'final epoch'. Number of epochs to train the model.Īn epoch is an iteration over the entire x and y If unspecified, batch_size will default to 32. If output layers in the model are named, you can also pass aĭictionary mapping output names to Numpy arrays. Or list of Numpy arrays (if the model has multiple outputs). If input layers in the model are named, you can also pass aĭictionary mapping input names to Numpy arrays.įramework-native tensors (e.g. Or list of Numpy arrays (if the model has multiple inputs). x: Numpy array of training data (if the model has a single input),.Trains the model for a fixed number of epochs (iterations on a dataset). Optimizer, loss, metrics or sample_weight_mode.įit fit(self, x=None, y=None, batch_size=None, epochs=1, verbose=1, callbacks=None, validation_split=0.0, validation_data=None, shuffle=True, class_weight=None, sample_weight=None, initial_epoch=0, steps_per_epoch=None, validation_steps=None) ValueError: In case of invalid arguments for.**kwargs: When using the Theano/CNTK backends, these arguments.Or a dict mapping output names to target tensors. It can beĪ single tensor (for a single-output model), a list of tensors, Numpy data for these targets at training time), youĬan specify them via the target_tensors argument. Target tensors (in turn, Keras will not expect external If instead you would like to use your own Model's target, which will be fed with the target data during target_tensors: By default, Keras will create placeholders for the.weighted_metrics: List of metrics to be evaluated and weightedīy sample_weight or class_weight during training and testing.Sample_weight_mode on each output by passing a If the model has multiple outputs, you can use a different None defaults to sample-wise weights (1D). Sample weighting (2D weights), set this to 'temporal'. sample_weight_mode: If you need to do timestep-wise.Output names (strings) to scalar coefficients. If a list, it is expected to have a 1:1 mapping Weighted by the loss_weights coefficients. Will then be the weighted sum of all individual losses, loss_weights: Optional list or dictionary specifying scalarĬoefficients (Python floats) to weight the loss contributions.Multi-output model, you could also pass a dictionary, To specify different metrics for different outputs of a metrics: List of metrics to be evaluated by the model.Will then be the sum of all individual losses. The loss value that will be minimized by the model On each output by passing a dictionary or a list of losses. If the model has multiple outputs, you can use a different loss

loss: String (name of objective function) or objective function.optimizer: String (name of optimizer) or optimizer instance.Methods compile compile(self, optimizer, loss=None, metrics=None, loss_weights=None, sample_weight_mode=None, weighted_metrics=None, target_tensors=None) model.outputs is the list of output tensors.model.inputs is the list of input tensors.model.layers is a flattened list of the layers comprising the model graph.In the case of multi-input or multi-output models, you can use lists as well: model = Model(inputs=, outputs=)įor a detailed introduction of what Model can do, read this guide to the Keras functional API. This model will include all layers required in the computation of b given a. In the functional API, given some input tensor(s) and output tensor(s), you can instantiate a Model via: from keras.models import Model

Keras data generator