flexflow/keras_exp/models/model.py [34:424]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - tracing_id = 100 class BaseModel(object): def __init__(self, inputs, onnx_model): self._ffconfig = ff.FFConfig() print("Python API batchSize(%d) workersPerNodes(%d) numNodes(%d)" %(self._ffconfig.batch_size, self._ffconfig.workers_per_node, self._ffconfig.num_nodes)) self._ffmodel = None self._onnx_model = onnx_model for node in onnx_model.graph.node: print(node) for input in onnx_model.graph.initializer: print(input.name, input.dims, len(input.dims)) # for input in onnx_model.graph.input: # print(input) self._input_tensors = [] for key in inputs: input_tensor = inputs[key] t = Tensor(ffconfig=self._ffconfig, key=key, shape=input_tensor.shape, dtype=input_tensor.dtype) self._input_tensors.append(t) self._loss = None self._label_type = None self._metrics = [] self._label_type = ff.DataType.DT_FLOAT self._my_onnx_model = None self._output_tensor = None self._num_samples = 0 self._input_dataloaders = [] self._input_dataloaders_dim = [] self._label_dataloader = 0 self._label_dataloader_dim = 0 global tracing_id self.__tracing_id = tracing_id tracing_id += 1 def compile(self, optimizer, loss=None, metrics=None, loss_weights=None, weighted_metrics=None, run_eagerly=None, comp_mode=None, **kwargs): if loss_weights != None: assert 0, "loss_weights is not supported" if weighted_metrics != None: assert 0, "weighted_metrics is not supported" if run_eagerly != None: assert 0, "run_eagerly is not supported" assert loss != None, "loss is None" if loss == 'categorical_crossentropy': self._loss = ff_keras_losses.CategoricalCrossentropy() elif loss == 'sparse_categorical_crossentropy': self._loss = ff_keras_losses.SparseCategoricalCrossentropy() self._label_type = ff.DataType.DT_INT32 elif loss == 'mean_squared_error': self._loss = ff_keras_losses.MeanSquaredError() else: assert 0, 'Unsupported loss' assert metrics != None, "metrics is None" assert isinstance(metrics, list) == True, 'Metrics should be a list' for metric in metrics: if metric == 'accuracy': self._metrics.append(ff_keras_metrics.Accuracy()) elif metric == 'categorical_crossentropy': self._metrics.append(ff_keras_metrics.CategoricalCrossentropy()) elif metric == 'sparse_categorical_crossentropy': self._metrics.append(ff_keras_metrics.SparseCategoricalCrossentropy()) elif metric == 'mean_squared_error': self._metrics.append(ff_keras_metrics.MeanSquaredError()) elif metric == 'root_mean_squared_error': self._metrics.append(ff_keras_metrics.RootMeanSquaredError()) elif metric == 'mean_absolute_error': self._metrics.append(ff_keras_metrics.MeanAbsoluteError()) else: assert 0, 'Unsupported metric' self._ffmodel = ff.FFModel(self._ffconfig) self._create_input_tensors() self._create_flexflow_layers() layers = self._ffmodel.get_layers() for l in layers: print(l, layers[l]) if isinstance(optimizer, tf_keras_optimizer.Optimizer) == True: if isinstance(optimizer, tf_keras_optimizer.SGD) == True: self._ffoptimizer = ff_keras_optimizer.SGD(learning_rate=optimizer.learning_rate.numpy(), momentum=optimizer.momentum.numpy(), nesterov=optimizer.nesterov) elif isinstance(optimizer, tf_keras_optimizer.Adam) == True: self._ffoptimizer = ff_keras_optimizer.Adam(learning_rate=optimizer.learning_rate.numpy(), beta_1=optimizer.beta_1.numpy(), beta_2=optimizer.beta_2.numpy(), epsilon=optimizer.epsilon.numpy()) else: assert 0, "Unsupported optimizer" elif type(optimizer) == str: if optimizer == 'SGD': self._ffoptimizer = ff_keras_optimizer.SGD() elif optimizer == 'Adam': self._ffoptimizer = ff_keras_optimizer.Adam() else: assert 0, "Unsupported optimizer" else: assert 0, "Unsupported optimizer" self._create_optimizer() metrics_type = [] for metric in self._metrics: metrics_type.append(metric.type) self._ffmodel.compile(optimizer=self._ffoptimizer.ffhandle, loss_type=self._loss.type, metrics=metrics_type, comp_mode=comp_mode) self._create_label_tensor() #TODO: finish API def 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, validation_batch_size=None, validation_freq=1, max_queue_size=10, workers=1, use_multiprocessing=False): if batch_size != None: assert self._ffconfig.batch_size == batch_size, "batch size is not correct use -b to set it" if validation_split != 0.0: assert 0, "validation_split is not supported" if validation_data != None: assert 0, "validation_data is not supported" if shuffle != True: assert 0, "shuffle is not supported" if class_weight != None: assert 0, "class_weight is not supported" if sample_weight != None: assert 0, "sample_weight is not supported" if initial_epoch != 0: assert 0, "initial_epoch is not supported" if steps_per_epoch != None: assert 0, "steps_per_epoch is not supported" if validation_steps != None: assert 0, "validation_steps is not supported" if validation_batch_size != None: assert 0, "validation_batch_size is not supported" if validation_freq != 1: assert 0, "validation_freq is not supported" if max_queue_size != 10: assert 0, "max_queue_size is not supported" if workers != 1: assert 0, "workers is not supported" if use_multiprocessing != False: assert 0, "use_multiprocessing is not supported" assert self._output_tensor != None, "tensor is not init" if (isinstance(x, list) == False): input_tensors = [x] else: input_tensors = x label_tensor = y self._verify_tensors(input_tensors, label_tensor) self._create_data_loaders(input_tensors, label_tensor) self._ffmodel.init_layers() self._train(epochs, callbacks, eval=False) def _create_label_tensor(self): label_ffhandle = self._ffmodel.label_tensor self._label_tensor = Tensor(ffconfig=self._ffconfig, batch_shape=(self._ffconfig.batch_size, 1), dtype=self._label_type) self._label_tensor.ffhandle = label_ffhandle def _create_input_tensors(self): idx = 0 for input_tensor in self._input_tensors: self._input_tensors[idx].create_ff_tensor(self._ffmodel) idx += 1 def _create_flexflow_layers(self): self._my_onnx_model = ONNXModelKeras(self._onnx_model, self._ffconfig, self._ffmodel) input_dict = {} for input_tensor in self._input_tensors: key = "input_" + str(input_tensor.key) input_dict[key] = input_tensor.ffhandle self._output_tensor = self._my_onnx_model.apply(self._ffmodel, input_dict) def _create_optimizer(self): assert self._ffoptimizer != None, "optimizer is not set" if (isinstance(self._ffoptimizer, ff_keras_optimizer.SGD) == True) or (isinstance(self._ffoptimizer, ff_keras_optimizer.Adam) == True): self._ffoptimizer.create_ffhandle(self._ffmodel) else: assert 0, "unknown optimizer" def _verify_tensors(self, input_arrays, label_array): assert len(input_arrays) == len(self._input_tensors), "check len of input tensors" # TODO: move check shape into another function for np_array, t in zip(input_arrays, self._input_tensors): np_shape = np_array.shape assert len(np_shape) == t.num_dims, "check input shape" for i in range(1, len(np_shape)): assert np_shape[i] == t.batch_shape[i], "check input dims" assert np_array.dtype == t.dtype_str, "check input dtype" np_shape = label_array.shape assert len(np_shape) == self._label_tensor.num_dims, "check label shape" for i in range(1, len(np_shape)): assert np_shape[i] == self._label_tensor.batch_shape[i], "check label dims" assert label_array.dtype == self._label_tensor.dtype_str def _create_data_loaders(self, x_trains, y_train): # Todo: check all num_samples, should be the same input_shape = x_trains[0].shape self._num_samples = input_shape[0] assert len(self._input_tensors) != 0, "input_tensor is not set" assert self._label_tensor != 0, "label_tensor is not set" idx = 0 for x_train in x_trains: dataloader = self._ffmodel.create_data_loader(self._input_tensors[idx].ffhandle, x_train) self._input_dataloaders.append(dataloader) self._input_dataloaders_dim.append(len(input_shape)) idx += 1 dataloader = self._ffmodel.create_data_loader(self._label_tensor.ffhandle, y_train) self._label_dataloader = dataloader self._label_dataloader_dim = len(input_shape) def _train(self, epochs, callbacks, eval=False): if callbacks != None: for callback in callbacks: callback.set_model(self) if callbacks != None: for callback in callbacks: callback.on_train_begin() ts_start = self._ffconfig.get_current_time() epoch = 0 epoch_flag = True self.__tracing_id += 1 while (epoch < epochs) and (epoch_flag == True): if callbacks != None: for callback in callbacks: callback.on_epoch_begin(epoch) for dataloader in self._input_dataloaders: dataloader.reset() self._label_dataloader.reset() self._ffmodel.reset_metrics() iterations = self._num_samples / self._ffconfig.batch_size for iter in range(0, int(iterations)): if callbacks != None: for callback in callbacks: callback.on_batch_begin(iter) for dataloader in self._input_dataloaders: dataloader.next_batch(self._ffmodel) self._label_dataloader.next_batch(self._ffmodel) self._ffconfig.begin_trace(self.__tracing_id) self._ffmodel.forward() # for layer in self._layers: # layer.ffhandle.forward(self._ffmodel) if eval == False: self._ffmodel.zero_gradients() self._ffmodel.backward() self._ffmodel.update() else: self._ffmodel.compute_metrics() self._ffconfig.end_trace(self.__tracing_id) if callbacks != None: for callback in callbacks: callback.on_batch_end(iter) if callbacks != None: for callback in callbacks: early_stop = callback.on_epoch_end(epoch) if early_stop == True: print("Accuracy reaches, now early stop, epoch: %d" %(epoch)) epoch_flag = False epoch += 1 ts_end = self._ffconfig.get_current_time() run_time = 1e-6 * (ts_end - ts_start); print("epochs %d, ELAPSED TIME = %.4fs, interations %d, samples %d, THROUGHPUT = %.2f samples/s\n" %(epochs, run_time, int(iterations), self._num_samples, self._num_samples * epochs / run_time)); if callbacks != None: for callback in callbacks: callback.on_train_end() class Model(tf_keras_Model): def __init__(self, inputs, outputs, name=None): super(Model, self).__init__(inputs=inputs, outputs=outputs, name=name) if (isinstance(inputs, dict) == True): onnx_model = keras2onnx.convert_keras(self, name) self._base_model = BaseModel(inputs=inputs, onnx_model=onnx_model) def compile(self, optimizer, loss=None, metrics=None, loss_weights=None, weighted_metrics=None, run_eagerly=None, **kwargs): self._base_model.compile(optimizer=optimizer, loss=loss, metrics=metrics, loss_weights=loss_weights, weighted_metrics=weighted_metrics, run_eagerly=run_eagerly, **kwargs) def 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, validation_batch_size=None, validation_freq=1, max_queue_size=10, workers=1, use_multiprocessing=False): self._base_model.fit(x=x, y=y, batch_size=batch_size, epochs=epochs, verbose=verbose, callbacks=callbacks, validation_split=validation_split, validation_data=validation_data, shuffle=shuffle, class_weight=class_weight, sample_weight=sample_weight, initial_epoch=initial_epoch, steps_per_epoch=steps_per_epoch, validation_steps=validation_steps, validation_batch_size=validation_batch_size, validation_freq=validation_freq, max_queue_size=max_queue_size, workers=workers, use_multiprocessing=use_multiprocessing) class Sequential(tf_keras_Model): def __init__(self, inputs, outputs, name=None): super(Sequential, self).__init__(inputs=inputs, outputs=outputs, name=name) if (isinstance(inputs, dict) == True): onnx_model = keras2onnx.convert_keras(self, name) self._base_model = BaseModel(inputs=inputs, onnx_model=onnx_model) def compile(self, optimizer, loss=None, metrics=None, loss_weights=None, weighted_metrics=None, run_eagerly=None, **kwargs): self._base_model.compile(optimizer=optimizer, loss=loss, metrics=metrics, loss_weights=loss_weights, weighted_metrics=weighted_metrics, run_eagerly=run_eagerly, **kwargs) def 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, validation_batch_size=None, validation_freq=1, max_queue_size=10, workers=1, use_multiprocessing=False): self._base_model.fit(x=x, y=y, batch_size=batch_size, epochs=epochs, verbose=verbose, callbacks=callbacks, validation_split=validation_split, validation_data=validation_data, shuffle=shuffle, class_weight=class_weight, sample_weight=sample_weight, initial_epoch=initial_epoch, steps_per_epoch=steps_per_epoch, validation_steps=validation_steps, validation_batch_size=validation_batch_size, validation_freq=validation_freq, max_queue_size=max_queue_size, workers=workers, use_multiprocessing=use_multiprocessing) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - python/flexflow/keras_exp/models/model.py [34:424]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - tracing_id = 100 class BaseModel(object): def __init__(self, inputs, onnx_model): self._ffconfig = ff.FFConfig() print("Python API batchSize(%d) workersPerNodes(%d) numNodes(%d)" %(self._ffconfig.batch_size, self._ffconfig.workers_per_node, self._ffconfig.num_nodes)) self._ffmodel = None self._onnx_model = onnx_model for node in onnx_model.graph.node: print(node) for input in onnx_model.graph.initializer: print(input.name, input.dims, len(input.dims)) # for input in onnx_model.graph.input: # print(input) self._input_tensors = [] for key in inputs: input_tensor = inputs[key] t = Tensor(ffconfig=self._ffconfig, key=key, shape=input_tensor.shape, dtype=input_tensor.dtype) self._input_tensors.append(t) self._loss = None self._label_type = None self._metrics = [] self._label_type = ff.DataType.DT_FLOAT self._my_onnx_model = None self._output_tensor = None self._num_samples = 0 self._input_dataloaders = [] self._input_dataloaders_dim = [] self._label_dataloader = 0 self._label_dataloader_dim = 0 global tracing_id self.__tracing_id = tracing_id tracing_id += 1 def compile(self, optimizer, loss=None, metrics=None, loss_weights=None, weighted_metrics=None, run_eagerly=None, comp_mode=None, **kwargs): if loss_weights != None: assert 0, "loss_weights is not supported" if weighted_metrics != None: assert 0, "weighted_metrics is not supported" if run_eagerly != None: assert 0, "run_eagerly is not supported" assert loss != None, "loss is None" if loss == 'categorical_crossentropy': self._loss = ff_keras_losses.CategoricalCrossentropy() elif loss == 'sparse_categorical_crossentropy': self._loss = ff_keras_losses.SparseCategoricalCrossentropy() self._label_type = ff.DataType.DT_INT32 elif loss == 'mean_squared_error': self._loss = ff_keras_losses.MeanSquaredError() else: assert 0, 'Unsupported loss' assert metrics != None, "metrics is None" assert isinstance(metrics, list) == True, 'Metrics should be a list' for metric in metrics: if metric == 'accuracy': self._metrics.append(ff_keras_metrics.Accuracy()) elif metric == 'categorical_crossentropy': self._metrics.append(ff_keras_metrics.CategoricalCrossentropy()) elif metric == 'sparse_categorical_crossentropy': self._metrics.append(ff_keras_metrics.SparseCategoricalCrossentropy()) elif metric == 'mean_squared_error': self._metrics.append(ff_keras_metrics.MeanSquaredError()) elif metric == 'root_mean_squared_error': self._metrics.append(ff_keras_metrics.RootMeanSquaredError()) elif metric == 'mean_absolute_error': self._metrics.append(ff_keras_metrics.MeanAbsoluteError()) else: assert 0, 'Unsupported metric' self._ffmodel = ff.FFModel(self._ffconfig) self._create_input_tensors() self._create_flexflow_layers() layers = self._ffmodel.get_layers() for l in layers: print(l, layers[l]) if isinstance(optimizer, tf_keras_optimizer.Optimizer) == True: if isinstance(optimizer, tf_keras_optimizer.SGD) == True: self._ffoptimizer = ff_keras_optimizer.SGD(learning_rate=optimizer.learning_rate.numpy(), momentum=optimizer.momentum.numpy(), nesterov=optimizer.nesterov) elif isinstance(optimizer, tf_keras_optimizer.Adam) == True: self._ffoptimizer = ff_keras_optimizer.Adam(learning_rate=optimizer.learning_rate.numpy(), beta_1=optimizer.beta_1.numpy(), beta_2=optimizer.beta_2.numpy(), epsilon=optimizer.epsilon.numpy()) else: assert 0, "Unsupported optimizer" elif type(optimizer) == str: if optimizer == 'SGD': self._ffoptimizer = ff_keras_optimizer.SGD() elif optimizer == 'Adam': self._ffoptimizer = ff_keras_optimizer.Adam() else: assert 0, "Unsupported optimizer" else: assert 0, "Unsupported optimizer" self._create_optimizer() metrics_type = [] for metric in self._metrics: metrics_type.append(metric.type) self._ffmodel.compile(optimizer=self._ffoptimizer.ffhandle, loss_type=self._loss.type, metrics=metrics_type, comp_mode=comp_mode) self._create_label_tensor() #TODO: finish API def 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, validation_batch_size=None, validation_freq=1, max_queue_size=10, workers=1, use_multiprocessing=False): if batch_size != None: assert self._ffconfig.batch_size == batch_size, "batch size is not correct use -b to set it" if validation_split != 0.0: assert 0, "validation_split is not supported" if validation_data != None: assert 0, "validation_data is not supported" if shuffle != True: assert 0, "shuffle is not supported" if class_weight != None: assert 0, "class_weight is not supported" if sample_weight != None: assert 0, "sample_weight is not supported" if initial_epoch != 0: assert 0, "initial_epoch is not supported" if steps_per_epoch != None: assert 0, "steps_per_epoch is not supported" if validation_steps != None: assert 0, "validation_steps is not supported" if validation_batch_size != None: assert 0, "validation_batch_size is not supported" if validation_freq != 1: assert 0, "validation_freq is not supported" if max_queue_size != 10: assert 0, "max_queue_size is not supported" if workers != 1: assert 0, "workers is not supported" if use_multiprocessing != False: assert 0, "use_multiprocessing is not supported" assert self._output_tensor != None, "tensor is not init" if (isinstance(x, list) == False): input_tensors = [x] else: input_tensors = x label_tensor = y self._verify_tensors(input_tensors, label_tensor) self._create_data_loaders(input_tensors, label_tensor) self._ffmodel.init_layers() self._train(epochs, callbacks, eval=False) def _create_label_tensor(self): label_ffhandle = self._ffmodel.label_tensor self._label_tensor = Tensor(ffconfig=self._ffconfig, batch_shape=(self._ffconfig.batch_size, 1), dtype=self._label_type) self._label_tensor.ffhandle = label_ffhandle def _create_input_tensors(self): idx = 0 for input_tensor in self._input_tensors: self._input_tensors[idx].create_ff_tensor(self._ffmodel) idx += 1 def _create_flexflow_layers(self): self._my_onnx_model = ONNXModelKeras(self._onnx_model, self._ffconfig, self._ffmodel) input_dict = {} for input_tensor in self._input_tensors: key = "input_" + str(input_tensor.key) input_dict[key] = input_tensor.ffhandle self._output_tensor = self._my_onnx_model.apply(self._ffmodel, input_dict) def _create_optimizer(self): assert self._ffoptimizer != None, "optimizer is not set" if (isinstance(self._ffoptimizer, ff_keras_optimizer.SGD) == True) or (isinstance(self._ffoptimizer, ff_keras_optimizer.Adam) == True): self._ffoptimizer.create_ffhandle(self._ffmodel) else: assert 0, "unknown optimizer" def _verify_tensors(self, input_arrays, label_array): assert len(input_arrays) == len(self._input_tensors), "check len of input tensors" # TODO: move check shape into another function for np_array, t in zip(input_arrays, self._input_tensors): np_shape = np_array.shape assert len(np_shape) == t.num_dims, "check input shape" for i in range(1, len(np_shape)): assert np_shape[i] == t.batch_shape[i], "check input dims" assert np_array.dtype == t.dtype_str, "check input dtype" np_shape = label_array.shape assert len(np_shape) == self._label_tensor.num_dims, "check label shape" for i in range(1, len(np_shape)): assert np_shape[i] == self._label_tensor.batch_shape[i], "check label dims" assert label_array.dtype == self._label_tensor.dtype_str def _create_data_loaders(self, x_trains, y_train): # Todo: check all num_samples, should be the same input_shape = x_trains[0].shape self._num_samples = input_shape[0] assert len(self._input_tensors) != 0, "input_tensor is not set" assert self._label_tensor != 0, "label_tensor is not set" idx = 0 for x_train in x_trains: dataloader = self._ffmodel.create_data_loader(self._input_tensors[idx].ffhandle, x_train) self._input_dataloaders.append(dataloader) self._input_dataloaders_dim.append(len(input_shape)) idx += 1 dataloader = self._ffmodel.create_data_loader(self._label_tensor.ffhandle, y_train) self._label_dataloader = dataloader self._label_dataloader_dim = len(input_shape) def _train(self, epochs, callbacks, eval=False): if callbacks != None: for callback in callbacks: callback.set_model(self) if callbacks != None: for callback in callbacks: callback.on_train_begin() ts_start = self._ffconfig.get_current_time() epoch = 0 epoch_flag = True self.__tracing_id += 1 while (epoch < epochs) and (epoch_flag == True): if callbacks != None: for callback in callbacks: callback.on_epoch_begin(epoch) for dataloader in self._input_dataloaders: dataloader.reset() self._label_dataloader.reset() self._ffmodel.reset_metrics() iterations = self._num_samples / self._ffconfig.batch_size for iter in range(0, int(iterations)): if callbacks != None: for callback in callbacks: callback.on_batch_begin(iter) for dataloader in self._input_dataloaders: dataloader.next_batch(self._ffmodel) self._label_dataloader.next_batch(self._ffmodel) self._ffconfig.begin_trace(self.__tracing_id) self._ffmodel.forward() # for layer in self._layers: # layer.ffhandle.forward(self._ffmodel) if eval == False: self._ffmodel.zero_gradients() self._ffmodel.backward() self._ffmodel.update() else: self._ffmodel.compute_metrics() self._ffconfig.end_trace(self.__tracing_id) if callbacks != None: for callback in callbacks: callback.on_batch_end(iter) if callbacks != None: for callback in callbacks: early_stop = callback.on_epoch_end(epoch) if early_stop == True: print("Accuracy reaches, now early stop, epoch: %d" %(epoch)) epoch_flag = False epoch += 1 ts_end = self._ffconfig.get_current_time() run_time = 1e-6 * (ts_end - ts_start); print("epochs %d, ELAPSED TIME = %.4fs, interations %d, samples %d, THROUGHPUT = %.2f samples/s\n" %(epochs, run_time, int(iterations), self._num_samples, self._num_samples * epochs / run_time)); if callbacks != None: for callback in callbacks: callback.on_train_end() class Model(tf_keras_Model): def __init__(self, inputs, outputs, name=None): super(Model, self).__init__(inputs=inputs, outputs=outputs, name=name) if (isinstance(inputs, dict) == True): onnx_model = keras2onnx.convert_keras(self, name) self._base_model = BaseModel(inputs=inputs, onnx_model=onnx_model) def compile(self, optimizer, loss=None, metrics=None, loss_weights=None, weighted_metrics=None, run_eagerly=None, **kwargs): self._base_model.compile(optimizer=optimizer, loss=loss, metrics=metrics, loss_weights=loss_weights, weighted_metrics=weighted_metrics, run_eagerly=run_eagerly, **kwargs) def 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, validation_batch_size=None, validation_freq=1, max_queue_size=10, workers=1, use_multiprocessing=False): self._base_model.fit(x=x, y=y, batch_size=batch_size, epochs=epochs, verbose=verbose, callbacks=callbacks, validation_split=validation_split, validation_data=validation_data, shuffle=shuffle, class_weight=class_weight, sample_weight=sample_weight, initial_epoch=initial_epoch, steps_per_epoch=steps_per_epoch, validation_steps=validation_steps, validation_batch_size=validation_batch_size, validation_freq=validation_freq, max_queue_size=max_queue_size, workers=workers, use_multiprocessing=use_multiprocessing) class Sequential(tf_keras_Model): def __init__(self, inputs, outputs, name=None): super(Sequential, self).__init__(inputs=inputs, outputs=outputs, name=name) if (isinstance(inputs, dict) == True): onnx_model = keras2onnx.convert_keras(self, name) self._base_model = BaseModel(inputs=inputs, onnx_model=onnx_model) def compile(self, optimizer, loss=None, metrics=None, loss_weights=None, weighted_metrics=None, run_eagerly=None, **kwargs): self._base_model.compile(optimizer=optimizer, loss=loss, metrics=metrics, loss_weights=loss_weights, weighted_metrics=weighted_metrics, run_eagerly=run_eagerly, **kwargs) def 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, validation_batch_size=None, validation_freq=1, max_queue_size=10, workers=1, use_multiprocessing=False): self._base_model.fit(x=x, y=y, batch_size=batch_size, epochs=epochs, verbose=verbose, callbacks=callbacks, validation_split=validation_split, validation_data=validation_data, shuffle=shuffle, class_weight=class_weight, sample_weight=sample_weight, initial_epoch=initial_epoch, steps_per_epoch=steps_per_epoch, validation_steps=validation_steps, validation_batch_size=validation_batch_size, validation_freq=validation_freq, max_queue_size=max_queue_size, workers=workers, use_multiprocessing=use_multiprocessing) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -