def model_builder(numhiddenlayers=3, init_mode='uniform', neurons=[400, 300, 200, 100, 50, 200, 200], acti='relu'): Model = Sequential() Model.add(Dense(neurons[0], input_shape=(DOF,), kernel_initializer=init_mode, activation=acti)) for i in range(0, numhiddenlayers): Model.add(Dense(neurons[i + 1], kernel_initializer=init_mode, activation=acti)) Model.add(Dense(POSOR)) return Model
def convolutional(instruction=None, read_mode=None, preprocess=True, data_path=None, verbose=0, new_folders=True, image_column=None, training_ratio=0.8, fine_tune=False, augmentation=True, custom_arch=None, pretrained=None, epochs=10, height=None, width=None, save_as_tfjs=None, save_as_tflite=None, generate_plots=True): ''' Body of the convolutional function used that is called in the neural network query if the data is presented in images. :param many parameters: used to preprocess, tune, plot generation, and parameterizing the convolutional neural network trained. :return dictionary that holds all the information for the finished model. ''' # data_path = get_folder_dir() logger("Generating datasets for classes") LR = 0.001 plots = {} if pretrained: if not height: height = 224 if not width: width = 224 if height != 224 or width != 224: raise ValueError( "For pretrained models, both 'height' and 'width' must be 224." ) if preprocess: if custom_arch: raise ValueError( "If 'custom_arch' is not None, 'preprocess' must be set to false." ) read_mode_info = set_distinguisher(data_path, read_mode) read_mode = read_mode_info["read_mode"] training_path = "/proc_training_set" testing_path = "/proc_testing_set" if read_mode == "setwise": processInfo = setwise_preprocessing(data_path, new_folders, height, width) if not new_folders: training_path = "/training_set" testing_path = "/testing_set" # if image dataset in form of csv elif read_mode == "csvwise": if training_ratio <= 0 or training_ratio >= 1: raise BaseException(f"Test ratio must be between 0 and 1.") processInfo = csv_preprocessing(read_mode_info["csv_path"], data_path, instruction, image_column, training_ratio, height, width) # if image dataset in form of one folder containing class folders elif read_mode == "classwise": if training_ratio <= 0 or training_ratio >= 1: raise BaseException(f"Test ratio must be between 0 and 1.") processInfo = classwise_preprocessing(data_path, training_ratio, height, width) else: training_path = "/training_set" testing_path = "/testing_set" processInfo = already_processed(data_path) num_channels = 3 color_mode = 'rgb' if processInfo["gray_scale"]: num_channels = 1 color_mode = 'grayscale' input_shape = (processInfo["height"], processInfo["width"], num_channels) input_single = (processInfo["height"], processInfo["width"]) num_classes = processInfo["num_categories"] loss_func = "" output_layer_activation = "" if num_classes > 2: loss_func = "categorical_crossentropy" output_layer_activation = "softmax" elif num_classes == 2: num_classes = 1 loss_func = "binary_crossentropy" output_layer_activation = "sigmoid" logger("Creating convolutional neural network dynamically") # Convolutional Neural Network # Build model based on custom_arch configuration if given if custom_arch: with open(custom_arch, "r") as f: custom_arch_dict = json.load(f) custom_arch_json_string = json.dumps(custom_arch_dict) model = model_from_json(custom_arch_json_string) # Build an existing state-of-the-art model elif pretrained: arch_lower = pretrained.get('arch').lower() # If user specifies value of pretrained['weights'] as 'imagenet', weights pretrained on ImageNet will be used if 'weights' in pretrained and pretrained.get('weights') == 'imagenet': # Load ImageNet pretrained weights if arch_lower == "vggnet16": base_model = VGG16(include_top=False, weights='imagenet', input_shape=input_shape) x = Flatten()(base_model.output) x = Dense(4096)(x) x = Dropout(0.5)(x) x = Dense(4096)(x) x = Dropout(0.5)(x) pred = Dense(num_classes, activation=output_layer_activation)(x) model = Model(base_model.input, pred) elif arch_lower == "vggnet19": base_model = VGG19(include_top=False, weights='imagenet', input_shape=input_shape) x = Flatten()(base_model.output) x = Dense(4096)(x) x = Dropout(0.5)(x) x = Dense(4096)(x) x = Dropout(0.5)(x) pred = Dense(num_classes, activation=output_layer_activation)(x) model = Model(base_model.input, pred) elif arch_lower == "resnet50": base_model = ResNet50(include_top=False, weights='imagenet', input_shape=input_shape) x = Flatten()(base_model.output) x = GlobalAveragePooling2D()(base_model.output) x = Dropout(0.5)(x) pred = Dense(num_classes, activation=output_layer_activation)(x) model = Model(base_model.input, pred) elif arch_lower == "resnet101": base_model = ResNet101(include_top=False, weights='imagenet', input_shape=input_shape) x = GlobalAveragePooling2D()(base_model.output) x = Dropout(0.5)(x) pred = Dense(num_classes, activation=output_layer_activation)(x) model = Model(base_model.input, pred) elif arch_lower == "resnet152": base_model = ResNet152(include_top=False, weights='imagenet', input_shape=input_shape) x = GlobalAveragePooling2D()(base_model.output) x = Dropout(0.5)(x) pred = Dense(num_classes, activation=output_layer_activation)(x) model = Model(base_model.input, pred) elif arch_lower == "mobilenet": base_model = MobileNet(include_top=False, weights='imagenet', input_shape=input_shape) x = fine_tuned_model(base_model) pred = Dense(num_classes, activation=output_layer_activation)(x) model = Model(base_model.input, pred) elif arch_lower == "mobilenetv2": base_model = MobileNetV2(include_top=False, weights='imagenet', input_shape=input_shape) x = fine_tuned_model(base_model) pred = Dense(num_classes, activation=output_layer_activation)(x) model = Model(base_model.input, pred) elif arch_lower == "densenet121": base_model = DenseNet121(include_top=False, weights='imagenet', input_shape=input_shape) x = fine_tuned_model(base_model) pred = Dense(num_classes, activation=output_layer_activation)(x) model = Model(base_model.input, pred) elif arch_lower == "densenet169": base_model = DenseNet169(include_top=False, weights='imagenet', input_shape=input_shape) x = fine_tuned_model(base_model) pred = Dense(num_classes, activation=output_layer_activation)(x) model = Model(base_model.input, pred) elif arch_lower == "densenet201": base_model = DenseNet201(include_top=False, weights='imagenet', input_shape=input_shape) x = fine_tuned_model(base_model) pred = Dense(num_classes, activation=output_layer_activation)(x) model = Model(base_model.input, pred) else: raise ModuleNotFoundError("arch \'" + pretrained.get('arch') + "\' not supported.") else: # Randomly initialized weights if arch_lower == "vggnet16": model = VGG16(include_top=True, weights=None, classes=num_classes, classifier_activation=output_layer_activation) elif arch_lower == "vggnet19": model = VGG19(include_top=True, weights=None, classes=num_classes, classifier_activation=output_layer_activation) elif arch_lower == "resnet50": model = ResNet50(include_top=True, weights=None, classes=num_classes) elif arch_lower == "resnet101": model = ResNet101(include_top=True, weights=None, classes=num_classes) elif arch_lower == "resnet152": model = ResNet152(include_top=True, weights=None, classes=num_classes) elif arch_lower == "mobilenet": model = MobileNet(include_top=True, weights=None, classes=num_classes) elif arch_lower == "mobilenetv2": model = MobileNetV2(include_top=True, weights=None, classes=num_classes) elif arch_lower == "densenet121": model = DenseNet121(include_top=True, weights=None, classes=num_classes) elif arch_lower == "densenet169": model = DenseNet169(include_top=True, weights=None, classes=num_classes) elif arch_lower == "densenet201": model = DenseNet201(include_top=True, weights=None, classes=num_classes) else: raise ModuleNotFoundError("arch \'" + pretrained.get('arch') + "\' not supported.") else: model = Sequential() # model.add( # Conv2D( # 64, # kernel_size=3, # activation="relu", # input_shape=input_shape)) # model.add(MaxPooling2D(pool_size=(2, 2))) # model.add(Conv2D(64, kernel_size=3, activation="relu")) # model.add(MaxPooling2D(pool_size=(2, 2))) # model.add(Flatten()) # model.add(Dense(num_classes, activation="softmax")) # model.compile( # optimizer="adam", # loss=loss_func, # metrics=['accuracy']) model.add( Conv2D(filters=64, kernel_size=5, activation="relu", input_shape=input_shape)) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(filters=64, kernel_size=3, activation="relu")) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Conv2D(filters=64, kernel_size=3, activation="relu")) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Flatten()) model.add(Dense(units=256, activation="relu")) model.add(Dropout(0.25)) model.add(Dense(units=num_classes, activation="softmax")) if pretrained and 'weights' in pretrained and pretrained.get( 'weights') == 'imagenet': for layer in base_model.layers: layer.trainable = False opt = Adam(learning_rate=LR) model.compile(optimizer=opt, loss=loss_func, metrics=['accuracy']) logger("Located image data") if augmentation: train_data = ImageDataGenerator(rescale=1. / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True) test_data = ImageDataGenerator(rescale=1. / 255) logger('Dataset augmented through zoom, shear, flip, and rescale') else: train_data = ImageDataGenerator() test_data = ImageDataGenerator() logger("->", "Optimal image size identified: {}".format(input_shape)) X_train = train_data.flow_from_directory( data_path + training_path, target_size=input_single, color_mode=color_mode, batch_size=(16 if processInfo["train_size"] >= 16 else 1), class_mode=loss_func[:loss_func.find("_")]) X_test = test_data.flow_from_directory( data_path + testing_path, target_size=input_single, color_mode=color_mode, batch_size=(16 if processInfo["test_size"] >= 16 else 1), class_mode=loss_func[:loss_func.find("_")]) if epochs <= 0: raise BaseException("Number of epochs has to be greater than 0.") print("\n") logger('Training image model') # model.summary() history = model.fit_generator( X_train, steps_per_epoch=X_train.n // X_train.batch_size, validation_data=X_test, validation_steps=X_test.n // X_test.batch_size, epochs=epochs, verbose=verbose) if fine_tune: logger( '->', 'Training accuracy: {}'.format( history.history['accuracy'][len(history.history['accuracy']) - 1])) logger( '->', 'Validation accuracy: {}'.format(history.history['val_accuracy'][ len(history.history['val_accuracy']) - 1])) for layer in base_model.layers: layer.trainable = True opt = Adam(learning_rate=LR / 10) model.compile(optimizer=opt, loss=loss_func, metrics=['accuracy']) print("\n\n") logger('Training fine tuned model') fine_tuning_epoch = epochs + 10 history_fine = model.fit_generator( X_train, steps_per_epoch=X_train.n // X_train.batch_size, validation_data=X_test, validation_steps=X_test.n // X_test.batch_size, epochs=fine_tuning_epoch, initial_epoch=history.epoch[-1], verbose=verbose) #frozen model acc and loss history acc = history.history['accuracy'] val_acc = history.history['val_accuracy'] loss = history.history['loss'] val_loss = history.history['val_loss'] #fine tuned model acc and loss history acc += history_fine.history['accuracy'] val_acc += history_fine.history['val_accuracy'] loss += history_fine.history['loss'] val_loss += history_fine.history['val_loss'] if generate_plots: plots = generate_fine_tuned_classification_plots( acc, val_acc, loss, val_loss, epochs) models = [] losses = [] accuracies = [] model_data = [] model_data.append(model) models.append(history) losses.append( history.history["val_loss"][len(history.history["val_loss"]) - 1]) accuracies.append( history.history['val_accuracy'][len(history.history['val_accuracy']) - 1]) # final_model = model_data[accuracies.index(max(accuracies))] # final_hist = models[accuracies.index(max(accuracies))] if generate_plots and not fine_tune: plots = generate_classification_plots(models[len(models) - 1]) print("\n") logger( '->', 'Final training accuracy: {}'.format( history.history['accuracy'][len(history.history['accuracy']) - 1])) logger( '->', 'Final validation accuracy: {}'.format(history.history['val_accuracy'][ len(history.history['val_accuracy']) - 1])) # storing values the model dictionary number_of_examples = len(X_test.filenames) number_of_generator_calls = math.ceil(number_of_examples / (1.0 * X_test.batch_size)) test_labels = [] for i in range(0, int(number_of_generator_calls)): test_labels.extend(np.array(X_test[i][1])) predIdx = model.predict(X_test) if output_layer_activation == "sigmoid": real = [int(x) for x in test_labels] ans = [] for i in range(len(predIdx)): ans.append(int(round(predIdx[i][0]))) elif output_layer_activation == "softmax": real = [] for ans in test_labels: real.append(ans.argmax()) ans = [] for r in predIdx: ans.append(r.argmax()) else: print("NOT THE CASE") logger("Stored model under 'convolutional_NN' key") if save_as_tfjs: tfjs.converters.save_keras_model(model, "tfjsmodel") logger("Saved tfjs model under 'tfjsmodel' directory") if save_as_tflite: converter = tf.lite.TFLiteConverter.from_keras_model(model) tflite_model = converter.convert() open("model.tflite", "wb").write(tflite_model) logger("Saved tflite model as 'model.tflite' ") clearLog() K.clear_session() return { 'id': generate_id(), 'data_type': read_mode, 'data_path': data_path, 'data': { 'train': X_train, 'test': X_test }, 'shape': input_shape, 'res': { 'real': real, 'ans': ans }, 'model': model, 'plots': plots, 'losses': { 'training_loss': history.history['loss'], 'val_loss': history.history['val_loss'] }, 'accuracy': { 'training_accuracy': history.history['accuracy'], 'validation_accuracy': history.history['val_accuracy'] }, 'num_classes': (2 if num_classes == 1 else num_classes), 'data_sizes': { 'train_size': processInfo['train_size'], 'test_size': processInfo['test_size'] } }
def convolutional(instruction=None, read_mode=None, preprocess=True, data_path=None, verbose=0, new_folders=True, image_column=None, training_ratio=0.8, augmentation=True, custom_arch=None, pretrained=None, epochs=10, height=None, width=None): ''' Body of the convolutional function used that is called in the neural network query if the data is presented in images. :param many parameters: used to preprocess, tune, plot generation, and parameterizing the convolutional neural network trained. :return dictionary that holds all the information for the finished model. ''' # data_path = get_folder_dir() logger("Generating datasets for classes") if pretrained: if not height: height = 224 if not width: width = 224 if height != 224 or width != 224: raise ValueError( "For pretrained models, both 'height' and 'width' must be 224." ) if preprocess: if custom_arch: raise ValueError( "If 'custom_arch' is not None, 'preprocess' must be set to false." ) read_mode_info = set_distinguisher(data_path, read_mode) read_mode = read_mode_info["read_mode"] training_path = "/proc_training_set" testing_path = "/proc_testing_set" if read_mode == "setwise": processInfo = setwise_preprocessing(data_path, new_folders, height, width) if not new_folders: training_path = "/training_set" testing_path = "/testing_set" # if image dataset in form of csv elif read_mode == "csvwise": if training_ratio <= 0 or training_ratio >= 1: raise BaseException(f"Test ratio must be between 0 and 1.") processInfo = csv_preprocessing(read_mode_info["csv_path"], data_path, instruction, image_column, training_ratio, height, width) # if image dataset in form of one folder containing class folders elif read_mode == "classwise": if training_ratio <= 0 or training_ratio >= 1: raise BaseException(f"Test ratio must be between 0 and 1.") processInfo = classwise_preprocessing(data_path, training_ratio, height, width) else: training_path = "/training_set" testing_path = "/testing_set" processInfo = already_processed(data_path) num_channels = 3 color_mode = 'rgb' if processInfo["gray_scale"]: num_channels = 1 color_mode = 'grayscale' input_shape = (processInfo["height"], processInfo["width"], num_channels) input_single = (processInfo["height"], processInfo["width"]) num_classes = processInfo["num_categories"] loss_func = "" output_layer_activation = "" if num_classes > 2: loss_func = "categorical_crossentropy" output_layer_activation = "softmax" elif num_classes == 2: num_classes = 1 loss_func = "binary_crossentropy" output_layer_activation = "sigmoid" logger("Creating convolutional neural netwwork dynamically") # Convolutional Neural Network # Build model based on custom_arch configuration if given if custom_arch: with open(custom_arch, "r") as f: custom_arch_dict = json.load(f) custom_arch_json_string = json.dumps(custom_arch_dict) model = model_from_json(custom_arch_json_string) # Build an existing state-of-the-art model elif pretrained: arch_lower = pretrained.get('arch').lower() # If user specifies value of pretrained['weights'] as 'imagenet', weights pretrained on ImageNet will be used if 'weights' in pretrained and pretrained.get('weights') == 'imagenet': # Load ImageNet pretrained weights if arch_lower == "vggnet16": base_model = VGG16(include_top=False, weights='imagenet', input_shape=input_shape) x = Flatten()(base_model.output) x = Dense(4096)(x) x = Dropout(0.5)(x) x = Dense(4096)(x) x = Dropout(0.5)(x) pred = Dense(num_classes, activation=output_layer_activation)(x) model = Model(base_model.input, pred) elif arch_lower == "vggnet19": base_model = VGG19(include_top=False, weights='imagenet', input_shape=input_shape) x = Flatten()(base_model.output) x = Dense(4096)(x) x = Dropout(0.5)(x) x = Dense(4096)(x) x = Dropout(0.5)(x) pred = Dense(num_classes, activation=output_layer_activation)(x) model = Model(base_model.input, pred) elif arch_lower == "resnet50": base_model = ResNet50(include_top=False, weights='imagenet', input_shape=input_shape) x = Flatten()(base_model.output) x = GlobalAveragePooling2D()(base_model.output) x = Dropout(0.5)(x) pred = Dense(num_classes, activation=output_layer_activation)(x) model = Model(base_model.input, pred) elif arch_lower == "resnet101": base_model = ResNet101(include_top=False, weights='imagenet', input_shape=input_shape) x = GlobalAveragePooling2D()(base_model.output) x = Dropout(0.5)(x) pred = Dense(num_classes, activation=output_layer_activation)(x) model = Model(base_model.input, pred) elif arch_lower == "resnet152": base_model = ResNet152(include_top=False, weights='imagenet', input_shape=input_shape) x = GlobalAveragePooling2D()(base_model.output) x = Dropout(0.5)(x) pred = Dense(num_classes, activation=output_layer_activation)(x) model = Model(base_model.input, pred) else: raise ModuleNotFoundError("arch \'" + pretrained.get('arch') + "\' not supported.") else: # Randomly initialized weights if arch_lower == "vggnet16": model = VGG16(include_top=True, weights=None, classes=num_classes, classifier_activation=output_layer_activation) elif arch_lower == "vggnet19": model = VGG19(include_top=True, weights=None, classes=num_classes, classifier_activation=output_layer_activation) elif arch_lower == "resnet50": model = ResNet50(include_top=True, weights=None, classes=num_classes) elif arch_lower == "resnet101": model = ResNet101(include_top=True, weights=None, classes=num_classes) elif arch_lower == "resnet152": model = ResNet152(include_top=True, weights=None, classes=num_classes) else: raise ModuleNotFoundError("arch \'" + pretrained.get('arch') + "\' not supported.") else: model = Sequential() # model.add( # Conv2D( # 64, # kernel_size=3, # activation="relu", # input_shape=input_shape)) # model.add(MaxPooling2D(pool_size=(2, 2))) # model.add(Conv2D(64, kernel_size=3, activation="relu")) # model.add(MaxPooling2D(pool_size=(2, 2))) # model.add(Flatten()) # model.add(Dense(num_classes, activation="softmax")) # model.compile( # optimizer="adam", # loss=loss_func, # metrics=['accuracy']) model.add( Conv2D(filters=64, kernel_size=5, activation="relu", input_shape=input_shape)) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(filters=64, kernel_size=3, activation="relu")) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Conv2D(filters=64, kernel_size=3, activation="relu")) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Flatten()) model.add(Dense(units=256, activation="relu")) model.add(Dropout(0.25)) model.add(Dense(units=num_classes, activation="softmax")) model.compile(optimizer="adam", loss=loss_func, metrics=['accuracy']) logger("Located image data") if augmentation: train_data = ImageDataGenerator(rescale=1. / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True) test_data = ImageDataGenerator(rescale=1. / 255) logger('Dataset augmented through zoom, shear, flip, and rescale') else: train_data = ImageDataGenerator() test_data = ImageDataGenerator() logger("->", "Optimal image size identified: {}".format(input_shape)) X_train = train_data.flow_from_directory( data_path + training_path, target_size=input_single, color_mode=color_mode, batch_size=(16 if processInfo["train_size"] >= 16 else 1), class_mode=loss_func[:loss_func.find("_")]) X_test = test_data.flow_from_directory( data_path + testing_path, target_size=input_single, color_mode=color_mode, batch_size=(16 if processInfo["test_size"] >= 16 else 1), class_mode=loss_func[:loss_func.find("_")]) if epochs <= 0: raise BaseException("Number of epochs has to be greater than 0.") logger('Training image model') history = model.fit_generator( X_train, steps_per_epoch=X_train.n // X_train.batch_size, validation_data=X_test, validation_steps=X_test.n // X_test.batch_size, epochs=epochs, verbose=verbose) logger( '->', 'Final training accuracy: {}'.format( history.history['accuracy'][len(history.history['accuracy']) - 1])) logger( '->', 'Final validation accuracy: {}'.format(history.history['val_accuracy'][ len(history.history['val_accuracy']) - 1])) # storing values the model dictionary logger("Stored model under 'convolutional_NN' key") clearLog() return { 'id': generate_id(), 'data_type': read_mode, 'data_path': data_path, 'data': { 'train': X_train, 'test': X_test }, 'shape': input_shape, "model": model, 'losses': { 'training_loss': history.history['loss'], 'val_loss': history.history['val_loss'] }, 'accuracy': { 'training_accuracy': history.history['accuracy'], 'validation_accuracy': history.history['val_accuracy'] }, 'num_classes': (2 if num_classes == 1 else num_classes), 'data_sizes': { 'train_size': processInfo['train_size'], 'test_size': processInfo['test_size'] } }
def model_builder(): Model = Sequential() Model.add( Dense(500, input_shape=(4, ), kernel_initializer="uniform", activation="sigmoid")) Model.add(Dense(500, kernel_initializer="uniform", activation="sigmoid")) Model.add(Dense(400, kernel_initializer="uniform", activation="sigmoid")) Model.add(Dense(400, kernel_initializer="uniform", activation="sigmoid")) Model.add(Dense(300, kernel_initializer="uniform", activation="sigmoid")) Model.add(Dense(3)) return Model
def create_model(**kwargs): # verbose verbose = kwargs.pop('verbose', False) # input shape img_width = kwargs.pop('img_width', 250) img_height = kwargs.pop('img_height', 250) if K.image_data_format() == 'channels_first': input_shape = (3, img_width, img_height) else: input_shape = (img_width, img_height, 3) # base model class + frozen layers base_model_class = kwargs.pop('base_model_class', None) tune_layers = kwargs.pop('tune_layers', None) if base_model_class: base_model = base_model_class(include_top=False, input_shape=input_shape) if tune_layers: for layer in base_model.layers[:-tune_layers]: layer.trainable = False # print(base_model.summary()) # classes (binary or multclass) no_classes = kwargs.pop('no_classes', 120) if no_classes == 2: final_activation = 'sigmoid' no_classes = 1 # binary classficication else: final_activation = 'softmax' # model if base_model_class: top_model = Sequential() top_model.add(Flatten(input_shape=base_model.output_shape[1:])) top_model.add(Dense(256)) top_model.add(Activation('relu')) top_model.add(Dropout(0.3)) # final layer top_model.add(Dense(no_classes)) top_model.add(Activation(final_activation)) model = Model(inputs=base_model.input, outputs=top_model(base_model.output)) else: model = Sequential() model.add(Conv2D(32, (3, 3), input_shape=input_shape)) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(32, (3, 3))) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(64, (3, 3))) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Flatten()) model.add(Dense(64)) model.add(Activation('relu')) model.add(Dropout(0.5)) # final layer model.add(Dense(no_classes)) model.add(Activation(final_activation)) # list of metrics metric_list = kwargs.pop('metric_list', []) # optimizer optimizer_class = kwargs.pop('optimizer_class', optimizers.Adam) optimizer_kwargs = kwargs.pop('optimizer_kwargs', {}) optimizer = optimizer_class(**optimizer_kwargs) # loss if no_classes <= 2: default_loss = 'binary_crossentropy' else: default_loss = 'categorical_crossentropy' loss = kwargs.pop('loss', default_loss) # compile model model.compile(loss=loss, optimizer=optimizer, metrics=metric_list) if verbose: # not so nice print('base_model is {}'.format(base_model.summary())) print('top_model is {}'.format(top_model.summary())) print('complete model is {}'.format(model.summary())) return model
x = Dropout(0.25)(x) x = Conv2D(64,(3,3),padding='same',activation='relu')(x) x = Conv2D(64,(3,3),activation='relu')(x) x = MaxPooling2D(pool_size=(2, 2))(x) x = Dropout(0.25)(x) x = Flatten()(x) x = Dense(512,activation='relu')(x) x = Dropout(0.5)(x) x = Dense(10,activation='softmax')(x) model = Model(input=inputs,output=x) #/----------------------------method1-API方法----------------------------- #/----------------------------method2-Sequential方法---------------------- model = Sequential() model.add(Conv2D(32,(3,3),padding='same',input_shape=x_train.shape[1:],activation='relu'))#第一层需要指出输入图像的大小 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),padding='same',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(512)) model.add(activation='relu') model.add(Dropout(0.5)) model.add(Dense(10)) model.add(activation='softmax')