def mnist_irnn(conf, input, **kw): result_sds = kw.pop('result_sds', None) project_id = kw.pop('project_id', None) f = conf['fit'] e = conf['evaluate'] x_train = input['x_tr'] y_train = input['y_tr'] x_val = input['x_te'] y_val = input['y_te'] x_test = input['x_te'] y_test = input['y_te'] x_train = x_train.reshape(x_train.shape[0], -1, 1) x_test = x_test.reshape(x_test.shape[0], -1, 1) x_val = x_test x_train_shape = x_train.shape input_shape = x_train_shape[1:] num_classes = y_train.shape[1] hidden_units = 100 learning_rate = 1e-6 with graph.as_default(): model = Sequential() model.add( SimpleRNN( hidden_units, kernel_initializer=initializers.RandomNormal(stddev=0.001), recurrent_initializer=initializers.Identity(gain=1.0), activation='relu', input_shape=input_shape)) model.add(Dense(num_classes)) model.add(Activation('softmax')) rmsprop = RMSprop(lr=learning_rate) model.compile(loss='categorical_crossentropy', optimizer=rmsprop, metrics=['accuracy']) # callback to save metrics batch_print_callback = LambdaCallback( on_epoch_end=lambda epoch, logs: logger_service.log_epoch_end( epoch, logs, result_sds, project_id)) # checkpoint to save best weight best_checkpoint = MongoModelCheckpoint(result_sds=result_sds, verbose=0, save_best_only=True) # checkpoint to save latest weight general_checkpoint = MongoModelCheckpoint(result_sds=result_sds, verbose=0) # training history = model.fit(x_train, y_train, validation_data=(x_val, y_val), callbacks=[ batch_print_callback, best_checkpoint, general_checkpoint ], verbose=0, **f['args']) score = model.evaluate(x_test, y_test, **e['args']) # weights = model.get_weights() config = model.get_config() logger_service.log_train_end(result_sds, model_config=config, score=score, history=history.history) return {'score': score, 'history': history.history}
def model_main(result_sds, project_id, result_dir, train_data_dir, validation_data_dir, nb_train_samples, nb_validation_samples, input_shape, img_width, img_height, epochs, batch_size): # 通过train_data_dir下的文件夹数目得到分类数量 l = os.listdir(train_data_dir) l.remove('.DS_Store') num_classes = len(l) if num_classes < 2: raise Exception('classes should be more than 1, put your ' 'different classes images file into ' 'different folder') # load the Xception network base_model = applications.Xception(weights='imagenet', include_top=False, input_shape=input_shape) # build the top of cnn network top_model = Sequential() top_model.add(Flatten(input_shape=base_model.output_shape[1:])) # top_model.add(Dense(256, activation='relu')) top_model.add(Dropout(0.5)) if num_classes == 2: top_model.add(Dense(1, activation='sigmoid')) model = Model(inputs=base_model.input, outputs=top_model(base_model.output)) # set the first 25 layers (up to the last conv block) # to non-trainable (weights will not be updated) for layer in model.layers[:-2]: layer.trainable = False model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=[ 'accuracy', custom_metrcis.matthews_correlation, custom_metrcis.precision, custom_metrcis.recall, custom_metrcis.fmeasure ]) else: top_model.add(Dense(num_classes, activation='softmax')) model = Model(inputs=base_model.input, outputs=top_model(base_model.output)) for layer in model.layers[:-2]: layer.trainable = False model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy']) # this is the augmentation configuration we will use for training train_datagen = ImageDataGenerator(rescale=1. / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True) # this is the augmentation configuration we will use for testing: # only rescaling test_datagen = ImageDataGenerator(rescale=1. / 255) if num_classes == 2: class_mode = 'binary' else: class_mode = 'categorical' train_generator = train_datagen.flow_from_directory( train_data_dir, target_size=(img_width, img_height), batch_size=batch_size, class_mode=class_mode) validation_generator = test_datagen.flow_from_directory( validation_data_dir, target_size=(img_width, img_height), batch_size=batch_size, class_mode=class_mode) # callback to save metrics batch_print_callback = LambdaCallback( on_epoch_begin=lambda epoch, logs: logger_service.log_epoch_begin( epoch, logs, result_sds, project_id), on_epoch_end=lambda epoch, logs: logger_service.log_epoch_end( epoch, logs, result_sds, project_id), on_batch_end=lambda batch, logs: logger_service.log_batch_end( batch, logs, result_sds, project_id)) # checkpoint to save best weight best_checkpoint = MyModelCheckpoint(os.path.abspath( os.path.join(result_dir, 'best.hdf5')), save_weights_only=True, verbose=1, save_best_only=True) # checkpoint to save latest weight general_checkpoint = MyModelCheckpoint(os.path.abspath( os.path.join(result_dir, 'latest.hdf5')), save_weights_only=True, verbose=1) history = model.fit_generator( train_generator, steps_per_epoch=nb_train_samples // batch_size, epochs=epochs, validation_data=validation_generator, validation_steps=nb_validation_samples // batch_size, callbacks=[batch_print_callback, best_checkpoint, general_checkpoint], ) # model.save_weights('first_try.h5') config = model.get_config() logger_service.log_train_end( result_sds, model_config=config, # score=score, history=history.history) keras_saved_model.save_model(result_dir, model) return {'history': history.history}
def convnet(conf, input, **kw): result_sds = kw.pop('result_sds', None) project_id = kw.pop('project_id', None) f = conf['fit'] e = conf['evaluate'] x_train = input['x_tr'] y_train = input['y_tr'] x_val = input['x_te'] y_val = input['y_te'] x_test = input['x_te'] y_test = input['y_te'] input_shape = x_train.shape[1:] output_units = y_train.shape[-1] with graph.as_default(): model = Sequential() model.add(Conv2D(32, (3, 3), activation='relu', input_shape=input_shape)) model.add(Conv2D(32, (3, 3), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(256, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(output_units, activation='softmax')) sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True) model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy']) # callback to save metrics batch_print_callback = LambdaCallback(on_epoch_end= lambda epoch, logs: logger_service.log_epoch_end(epoch, logs, result_sds, project_id)) # checkpoint to save best weight best_checkpoint = MongoModelCheckpoint(result_sds=result_sds, verbose=0, save_best_only=True) # checkpoint to save latest weight general_checkpoint = MongoModelCheckpoint(result_sds=result_sds, verbose=0) # training history = model.fit(x_train, y_train, validation_data=(x_val, y_val), callbacks=[batch_print_callback, best_checkpoint, general_checkpoint], verbose=0, **f['args']) score = model.evaluate(x_test, y_test, **e['args']) config = model.get_config() logger_service.log_train_end(result_sds, model_config=config, score=score, history=history.history) return {'score': score, 'history': history.history}
def mnist_mlp(conf, input, **kw): result_sds = kw.pop('result_sds', None) project_id = kw.pop('project_id', None) f = conf['fit'] e = conf['evaluate'] x_train = input['x_tr'] y_train = input['y_tr'] x_val = input['x_te'] y_val = input['y_te'] x_test = input['x_te'] y_test = input['y_te'] num_classes = y_train.shape[1] # 获取 img 的格式 x_train_shape = x_train.shape if x_train_shape[1] > 3: # 格式为 (None, img_rows, img_cols, 1) x_train = x_train.reshape(-1, x_train_shape[1] * x_train_shape[2]) x_test = x_test.reshape(-1, x_train_shape[1] * x_train_shape[2]) x_val = x_test else: # 格式为 (None, 1, img_rows, img_cols) x_train = x_train.reshape(-1, x_train_shape[2] * x_train_shape[3]) x_test = x_test.reshape(-1, x_train_shape[2] * x_train_shape[3]) x_val = x_test # print(x_train.shape) with graph.as_default(): model = Sequential() model.add( Dense(512, activation='relu', input_shape=(x_train.shape[1], ))) model.add(Dropout(0.2)) model.add(Dense(512, activation='relu')) model.add(Dropout(0.2)) model.add(Dense(num_classes, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer=RMSprop(), metrics=['accuracy']) # callback to save metrics batch_print_callback = LambdaCallback( on_epoch_end=lambda epoch, logs: logger_service.log_epoch_end( epoch, logs, result_sds, project_id)) # checkpoint to save best weight best_checkpoint = MongoModelCheckpoint(result_sds=result_sds, verbose=0, save_best_only=True) # checkpoint to save latest weight general_checkpoint = MongoModelCheckpoint(result_sds=result_sds, verbose=0) # training history = model.fit(x_train, y_train, validation_data=(x_val, y_val), callbacks=[ batch_print_callback, best_checkpoint, general_checkpoint ], verbose=0, **f['args']) score = model.evaluate(x_test, y_test, **e['args']) # weights = model.get_weights() config = model.get_config() logger_service.log_train_end(result_sds, model_config=config, score=score, history=history.history) return {'score': score, 'history': history.history}
def mlp_main(result_sds, project_id, job_id, user_ID, result_dir, x_train, y_train, x_val, y_val, x_test, y_test, f=None, e=None): training_logger = logger_service.TrainingLogger(f['args']['epochs'], project_id, job_id, user_ID, result_sds) input_len = x_train.shape[1] output_len = y_train.shape[1] model = Sequential() # Dense(64) is a fully-connected layer with 64 hidden units. # in the first layer, you must specify the expected input data shape: # here, 20-dimensional vectors. model.add(Dense(64, activation='relu', input_dim=input_len)) model.add(Dropout(0.5)) model.add(Dense(64, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(output_len, activation='softmax')) sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True) model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy']) # callback to save metrics # TODO custom to make database writing async batch_print_callback = LambdaCallback( on_epoch_begin=lambda epoch, logs: training_logger.log_epoch_begin( epoch, logs), on_epoch_end=lambda epoch, logs: training_logger.log_epoch_end( epoch, logs), on_batch_end=lambda batch, logs: training_logger.log_batch_end( batch, logs)) # checkpoint to save best weight best_checkpoint = MyModelCheckpoint(os.path.abspath( os.path.join(result_dir, 'best.hdf5')), save_weights_only=True, verbose=1, save_best_only=True) # checkpoint to save latest weight general_checkpoint = MyModelCheckpoint(os.path.abspath( os.path.join(result_dir, 'latest.hdf5')), save_weights_only=True, verbose=1) # training history = model.fit( x_train, y_train, validation_data=(x_val, y_val), callbacks=[batch_print_callback, best_checkpoint, general_checkpoint], verbose=1, **f['args']) score = model.evaluate(x_test, y_test, **e['args']) # weights = model.get_weights() config = model.get_config() logger_service.log_train_end(result_sds, model_config=config, score=score, history=history.history) keras_saved_model.save_model(result_dir, model) return {'score': score, 'history': history.history}
def keras_seq(conf, input, **kw): """ a general implementation of sequential model of keras :param conf: config dict :return: """ result_sds = kw.pop('result_sds', None) project_id = kw.pop('project_id', None) job_id = kw.pop('job_id', None) project = project_business.get_by_id(project_id) ow = ownership_business.get_ownership_by_owned_item(project, 'project') user_ID = ow.user.user_ID print('conf') print(conf) result_dir = kw.pop('result_dir', None) if result_sds is None: raise RuntimeError('no result sds id passed to model') if project_id is None: raise RuntimeError('no project id passed to model') with graph.as_default(): model = Sequential() ls = conf['layers'] comp = conf['compile'] f = conf['fit'] e = conf['evaluate'] x_train = input['x_tr'] y_train = input['y_tr'] x_val = input['x_te'] y_val = input['y_te'] x_test = input['x_te'] y_test = input['y_te'] training_logger = logger_service.TrainingLogger(f['args']['epochs'], project_id, job_id, user_ID, result_sds) # TODO add validator # op = comp['optimizer'] # loop to add layers for l in ls: # get layer class from keras layer_class = getattr(layers, l['name']) # add layer model.add(layer_class(**l['args'])) # optimiser # sgd_class = getattr(optimizers, op['name']) # sgd = sgd_class(**op['args']) # define the metrics # compile model.compile(**comp['args']) # callback to save metrics batch_print_callback = LambdaCallback(on_epoch_begin= lambda epoch, logs: training_logger.log_epoch_begin( epoch, logs), on_epoch_end= lambda epoch, logs: training_logger.log_epoch_end( epoch, logs), on_batch_end= lambda batch, logs: training_logger.log_batch_end( batch, logs) ) # checkpoint to save best weight best_checkpoint = MyModelCheckpoint( os.path.abspath(os.path.join(result_dir, 'best.hdf5')), save_weights_only=True, verbose=1, save_best_only=True) # checkpoint to save latest weight general_checkpoint = MyModelCheckpoint( os.path.abspath(os.path.join(result_dir, 'latest.hdf5')), save_weights_only=True, verbose=1) # training history = model.fit(x_train, y_train, validation_data=(x_val, y_val), callbacks=[batch_print_callback, best_checkpoint, general_checkpoint], verbose=0, **f['args']) # testing score = model.evaluate(x_test, y_test, **e['args']) # weights = model.get_weights() config = model.get_config() logger_service.log_train_end(result_sds, model_config=config, score=score, history=history.history) keras_saved_model.save_model(result_dir, model) return {'score': score, 'history': history.history}
def imdb_lstm(conf, input, **kw): result_sds = kw.pop('result_sds', None) project_id = kw.pop('project_id', None) f = conf['fit'] e = conf['evaluate'] x_train = input['x_tr'] y_train = input['y_tr'] x_val = input['x_te'] y_val = input['y_te'] x_test = input['x_te'] y_test = input['y_te'] max_features = input['max_features'] maxlen = input['maxlen'] x_train = sequence.pad_sequences(x_train, maxlen=maxlen) x_test = sequence.pad_sequences(x_test, maxlen=maxlen) x_val = x_test with graph.as_default(): model = Sequential() model.add(Embedding(max_features, 128)) model.add(LSTM(128, dropout=0.2, recurrent_dropout=0.2)) model.add(Dense(1, activation='sigmoid')) # try using different optimizers and different optimizer configs model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) # callback to save metrics batch_print_callback = LambdaCallback( on_epoch_end=lambda epoch, logs: logger_service.log_epoch_end( epoch, logs, result_sds, project_id)) # checkpoint to save best weight best_checkpoint = MongoModelCheckpoint(result_sds=result_sds, verbose=0, save_best_only=True) # checkpoint to save latest weight general_checkpoint = MongoModelCheckpoint(result_sds=result_sds, verbose=0) # training history = model.fit(x_train, y_train, validation_data=(x_val, y_val), callbacks=[ batch_print_callback, best_checkpoint, general_checkpoint ], verbose=0, **f['args']) score = model.evaluate(x_test, y_test, **e['args']) # weights = model.get_weights() config = model.get_config() logger_service.log_train_end(result_sds, model_config=config, score=score, history=history.history) return {'score': score, 'history': history.history}
def imdb_fasttext(conf, input, **kw): result_sds = kw.pop('result_sds', None) project_id = kw.pop('project_id', None) f = conf['fit'] e = conf['evaluate'] x_train = input['x_tr'] y_train = input['y_tr'] x_val = input['x_te'] y_val = input['y_te'] x_test = input['x_te'] y_test = input['y_te'] # Set parameters: # ngram_range = 2 will add bi-grams features ngram_range = input['ngram_range'] max_features = input['max_features'] maxlen = input['maxlen'] embedding_dims = 50 def create_ngram_set(input_list, ngram_value=2): """ Extract a set of n-grams from a list of integers. create_ngram_set([1, 4, 9, 4, 1, 4], ngram_value=2) {(4, 9), (4, 1), (1, 4), (9, 4)} create_ngram_set([1, 4, 9, 4, 1, 4], ngram_value=3) [(1, 4, 9), (4, 9, 4), (9, 4, 1), (4, 1, 4)] """ return set(zip(*[input_list[i:] for i in range(ngram_value)])) def add_ngram(sequences, token_indice, ngram_range=2): """ Augment the input list of list (sequences) by appending n-grams values. Example: adding bi-gram sequences = [[1, 3, 4, 5], [1, 3, 7, 9, 2]] token_indice = {(1, 3): 1337, (9, 2): 42, (4, 5): 2017} add_ngram(sequences, token_indice, ngram_range=2) [[1, 3, 4, 5, 1337, 2017], [1, 3, 7, 9, 2, 1337, 42]] Example: adding tri-gram sequences = [[1, 3, 4, 5], [1, 3, 7, 9, 2]] token_indice = {(1, 3): 1337, (9, 2): 42, (4, 5): 2017, (7, 9, 2): 2018} add_ngram(sequences, token_indice, ngram_range=3) [[1, 3, 4, 5, 1337], [1, 3, 7, 9, 2, 1337, 2018]] """ new_sequences = [] for input_list in sequences: new_list = input_list[:] for i in range(len(new_list) - ngram_range + 1): for ngram_value in range(2, ngram_range + 1): ngram = tuple(new_list[i:i + ngram_value]) if ngram in token_indice: new_list.append(token_indice[ngram]) new_sequences.append(new_list) return new_sequences if ngram_range > 1: print('Adding {}-gram features'.format(ngram_range)) # Create set of unique n-gram from the training set. ngram_set = set() for input_list in x_train: for i in range(2, ngram_range + 1): set_of_ngram = create_ngram_set(input_list, ngram_value=i) ngram_set.update(set_of_ngram) # Dictionary mapping n-gram token to a unique integer. # Integer values are greater than max_features in order # to avoid collision with existing features. start_index = max_features + 1 token_indice = {v: k + start_index for k, v in enumerate(ngram_set)} indice_token = {token_indice[k]: k for k in token_indice} # max_features is the highest integer that could be found in the # dataset. max_features = np.max(list(indice_token.keys())) + 1 # Augmenting x_train and x_test with n-grams features x_train = add_ngram(x_train, token_indice, ngram_range) x_test = add_ngram(x_test, token_indice, ngram_range) # print('Average train sequence length: {}'.format( # np.mean(list(map(len, x_train)), dtype=int))) # print('Average test sequence length: {}'.format( # np.mean(list(map(len, x_test)), dtype=int))) print('Pad sequences (samples x time)') x_train = sequence.pad_sequences(x_train, maxlen=maxlen) x_test = sequence.pad_sequences(x_test, maxlen=maxlen) x_val = x_test print('x_train shape:', x_train.shape) print('x_test shape:', x_test.shape) with graph.as_default(): model = Sequential() # we start off with an efficient embedding layer which maps # our vocab indices into embedding_dims dimensions model.add(Embedding(max_features, embedding_dims, input_length=maxlen)) # we add a GlobalAveragePooling1D, which will average the embeddings # of all words in the document model.add(GlobalAveragePooling1D()) # We project onto a single unit output layer, and squash it with a # sigmoid: model.add(Dense(1, activation='sigmoid')) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) # callback to save metrics batch_print_callback = LambdaCallback(on_epoch_end= lambda epoch, logs: logger_service.log_epoch_end( epoch, logs, result_sds, project_id)) # checkpoint to save best weight best_checkpoint = MongoModelCheckpoint(result_sds=result_sds, verbose=0, save_best_only=True) # checkpoint to save latest weight general_checkpoint = MongoModelCheckpoint(result_sds=result_sds, verbose=0) # training history = model.fit(x_train, y_train, validation_data=(x_val, y_val), callbacks=[batch_print_callback, best_checkpoint, general_checkpoint], verbose=0, **f['args']) score = model.evaluate(x_test, y_test, **e['args']) # weights = model.get_weights() config = model.get_config() logger_service.log_train_end(result_sds, model_config=config, score=score, history=history.history) return { 'score': score, 'history': history.history}
def keras_fmin_fnct(space): with graph.as_default(): model = Sequential() model.add(Dense(units=space['units'], activation=space['activation'], input_shape=[4])) model.add(Dropout(rate=space['rate'])) model.add(Dense(units=2, activation='softmax')) model.compile(optimizer=SGD(lr=space['lr']), loss=space['loss'], metrics=['acc']) model.fit(x_train, y_train, validation_data=(x_test, y_test), batch_size=128, epochs=10, verbose=0) score, acc = model.evaluate(x_test, y_test, batch_size=128) return {'loss': -acc, 'status': STATUS_OK, 'model': model}
def imdb_cnn_lstm(conf, input, **kw): result_sds = kw.pop('result_sds', None) project_id = kw.pop('project_id', None) f = conf['fit'] e = conf['evaluate'] x_train = input['x_tr'] y_train = input['y_tr'] x_val = input['x_te'] y_val = input['y_te'] x_test = input['x_te'] y_test = input['y_te'] # Embedding embedding_size = 128 max_features = input['max_features'] maxlen = input['maxlen'] x_train = sequence.pad_sequences(x_train, maxlen=maxlen) x_test = sequence.pad_sequences(x_test, maxlen=maxlen) x_val = x_test # set parameters: # Convolution kernel_size = 5 filters = 64 pool_size = 4 # LSTM lstm_output_size = 70 with graph.as_default(): model = Sequential() model.add(Embedding(max_features, embedding_size, input_length=maxlen)) model.add(Dropout(0.25)) model.add(Conv1D(filters, kernel_size, padding='valid', activation='relu', strides=1)) model.add(MaxPooling1D(pool_size=pool_size)) model.add(LSTM(lstm_output_size)) model.add(Dense(1)) model.add(Activation('sigmoid')) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) # callback to save metrics batch_print_callback = LambdaCallback(on_epoch_end= lambda epoch, logs: logger_service.log_epoch_end( epoch, logs, result_sds, project_id)) # checkpoint to save best weight best_checkpoint = MongoModelCheckpoint(result_sds=result_sds, verbose=0, save_best_only=True) # checkpoint to save latest weight general_checkpoint = MongoModelCheckpoint(result_sds=result_sds, verbose=0) # training history = model.fit(x_train, y_train, validation_data=(x_val, y_val), callbacks=[batch_print_callback, best_checkpoint, general_checkpoint], verbose=0, **f['args']) score = model.evaluate(x_test, y_test, **e['args']) # weights = model.get_weights() config = model.get_config() logger_service.log_train_end(result_sds, model_config=config, score=score, history=history.history) return { 'score': score, 'history': history.history}
def imdb_cnn(conf, input, **kw): result_sds = kw.pop('result_sds', None) project_id = kw.pop('project_id', None) f = conf['fit'] e = conf['evaluate'] x_train = input['x_tr'] y_train = input['y_tr'] x_val = input['x_te'] y_val = input['y_te'] x_test = input['x_te'] y_test = input['y_te'] max_features = input['max_features'] maxlen = input['maxlen'] x_train = sequence.pad_sequences(x_train, maxlen=maxlen) x_test = sequence.pad_sequences(x_test, maxlen=maxlen) x_val = x_test # set parameters: embedding_dims = 50 filters = 250 kernel_size = 3 hidden_dims = 250 with graph.as_default(): model = Sequential() model.add(Embedding(max_features, embedding_dims, input_length=maxlen)) model.add(Dropout(0.2)) # we add a Convolution1D, which will learn filters # word group filters of size filter_length: model.add( Conv1D(filters, kernel_size, padding='valid', activation='relu', strides=1)) # we use max pooling: model.add(GlobalMaxPooling1D()) # We add a vanilla hidden layer: model.add(Dense(hidden_dims)) model.add(Dropout(0.2)) model.add(Activation('relu')) # We project onto a single unit output layer, and squash it with a # sigmoid: model.add(Dense(1)) model.add(Activation('sigmoid')) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) # callback to save metrics batch_print_callback = LambdaCallback( on_epoch_end=lambda epoch, logs: logger_service.log_epoch_end( epoch, logs, result_sds, project_id)) # checkpoint to save best weight best_checkpoint = MongoModelCheckpoint(result_sds=result_sds, verbose=0, save_best_only=True) # checkpoint to save latest weight general_checkpoint = MongoModelCheckpoint(result_sds=result_sds, verbose=0) # training history = model.fit(x_train, y_train, validation_data=(x_val, y_val), callbacks=[ batch_print_callback, best_checkpoint, general_checkpoint ], verbose=0, **f['args']) score = model.evaluate(x_test, y_test, **e['args']) # weights = model.get_weights() config = model.get_config() logger_service.log_train_end(result_sds, model_config=config, score=score, history=history.history) return {'score': score, 'history': history.history}