我们从Python开源项目中，提取了以下13个代码示例，用于说明如何使用keras.optimizers.Adamax()。
def getOptimizer(optim, exp_decay, grad_norm_clip, lr = 0.001): """Function for setting up optimizer, combines several presets from published well performing models on SQuAD.""" optimizers = { 'Adam': Adam(lr=lr, decay=exp_decay, clipnorm=grad_norm_clip), 'Adamax': Adamax(lr=lr, decay=exp_decay, clipnorm=grad_norm_clip), 'Adadelta': Adadelta(lr=1.0, rho=0.95, epsilon=1e-06, decay=exp_decay, clipnorm=grad_norm_clip) } try: optimizer = optimizers[optim] except KeyError as e: raise ValueError('problems with defining optimizer: {}'.format(e.args[0])) del (optimizers) return optimizer # ------------------------------------------------------------------------------ # Data/model utilities. # ------------------------------------------------------------------------------
def __init__(self): filters1 = [16, 32, 64] # filters1 = [4, 8, 16, 32, 64, 128, 256] filters2 = [16, 32, 64] # filters2 = [4, 8, 16, 32, 64, 128, 256] losses1 = [losses.MSE, losses.MAE, losses.hinge, losses.categorical_crossentropy] # losses1 = [losses.MSE, losses.MAE, losses.hinge, losses.categorical_crossentropy] optimizers1 = [optimizers.Adam()] # optimizers1 = [optimizers.Adadelta(), optimizers.Adagrad(), optimizers.Adam(), optimizers.Adamax(), optimizers.SGD(), optimizers.RMSprop()] units1 = [16, 32, 64] # units1 = [4, 8, 16, 32, 64, 128, 256] kernel_sizes1 = [(3, 3)] # kernel_sizes = [(3, 3), (5, 5)] dropouts1 = [0.25] # dropouts1 = [0.25, 0.5, 0.75] dropouts2 = [0.5] # dropouts2 = [0.25, 0.5, 0.75] pool_sizes1 = [(2, 2)] # pool_sizes1 = [(2, 2)] # create standard experiments structure self.experiments = {"filters1": filters1, "filters2": filters2, "losses1": losses1, "units1": units1, "optimizers1": optimizers1, "kernel_sizes1": kernel_sizes1, "dropouts1": dropouts1, "dropouts2": dropouts2, "pool_sizes1": pool_sizes1}
def get_optimizer(name='Adadelta'): if name == 'SGD': return optimizers.SGD(clipnorm=1.) if name == 'RMSprop': return optimizers.RMSprop(clipnorm=1.) if name == 'Adagrad': return optimizers.Adagrad(clipnorm=1.) if name == 'Adadelta': return optimizers.Adadelta(clipnorm=1.) if name == 'Adam': return optimizers.Adam(clipnorm=1.) if name == 'Adamax': return optimizers.Adamax(clipnorm=1.) if name == 'Nadam': return optimizers.Nadam(clipnorm=1.) return optimizers.Adam(clipnorm=1.)
def get_optimizer(args): clipvalue = 0 clipnorm = 10 if args.algorithm == 'rmsprop': optimizer = opt.RMSprop(lr=0.001, rho=0.9, epsilon=1e-06, clipnorm=clipnorm, clipvalue=clipvalue) elif args.algorithm == 'sgd': optimizer = opt.SGD(lr=0.01, momentum=0.0, decay=0.0, nesterov=False, clipnorm=clipnorm, clipvalue=clipvalue) elif args.algorithm == 'adagrad': optimizer = opt.Adagrad(lr=0.01, epsilon=1e-06, clipnorm=clipnorm, clipvalue=clipvalue) elif args.algorithm == 'adadelta': optimizer = opt.Adadelta(lr=1.0, rho=0.95, epsilon=1e-06, clipnorm=clipnorm, clipvalue=clipvalue) elif args.algorithm == 'adam': optimizer = opt.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, clipnorm=clipnorm, clipvalue=clipvalue) elif args.algorithm == 'adamax': optimizer = opt.Adamax(lr=0.002, beta_1=0.9, beta_2=0.999, epsilon=1e-08, clipnorm=clipnorm, clipvalue=clipvalue) return optimizer
def create_model(img_rows, img_cols): model = Sequential() #initialize model model.add(Convolution2D(4, 3, 3, border_mode='same', activation='relu', init='he_normal', input_shape=(1, img_rows, img_cols))) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Convolution2D(8, 3, 3, border_mode='same', activation='relu', init='he_normal')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(2)) model.add(Activation('softmax')) adm = Adamax() #sgd = SGD(lr=1e-3, decay=1e-6, momentum=0.9, nesterov=True) model.compile(optimizer=adm, loss='categorical_crossentropy') return model
def test_adamax(): _test_optimizer(Adamax()) _test_optimizer(Adamax(decay=1e-3))
def get_learning_rate(self): if hasattr(self.model, 'optimizer'): config = self.model.optimizer.get_config() from keras.optimizers import Adadelta, Adam, Adamax, Adagrad, RMSprop, SGD if isinstance(self.model.optimizer, Adadelta) or isinstance(self.model.optimizer, Adam) \ or isinstance(self.model.optimizer, Adamax) or isinstance(self.model.optimizer, Adagrad)\ or isinstance(self.model.optimizer, RMSprop) or isinstance(self.model.optimizer, SGD): return config['lr'] * (1. / (1. + config['decay'] * float(K.get_value(self.model.optimizer.iterations)))) elif 'lr' in config: return config['lr']
def train_mlp1(x_train, y_train, x_test, y_test, input_dim, num_classes=24): """ :param x_train: :param y_train: :param x_test: :param y_test: :param input_dim: :param num_classes: :return: """ model = Sequential() model.add(Dense(512, input_dim=input_dim)) model.add(Activation('relu')) # An "activation" is just a non-linear function applied to the output of the layer # above. Here, with a "rectified linear unit", we clamp all values below 0 to 0. model.add(Dropout(0.1)) # Dropout helps protect the model from memorizing or "overfitting" the training data model.add(Dense(1024)) model.add(Activation('relu')) model.add(Dropout(0.1)) model.add(Dense(386)) model.add(Activation('relu')) model.add(Dropout(0.1)) model.add(Dense(num_classes)) model.add(Activation256('softmax')) # This special "softmax" activation among other things, # ensures the output is a valid probability distribution, that is # that its values are all non-negative and sum to 1. model.compile(loss='categorical_crossentropy', optimizer=Adamax(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=1e-5), metrics=["accuracy"]) model.fit(x_train, y_train, batch_size=40, nb_epoch=16, verbose=1, validation_data=(x_test, y_test)) score = model.evaluate(x_test, y_test, verbose=1) return score[1]
def train_cnn1(x_train, y_train, x_test, y_test, num_classes, input_shape): nb_filters = 32 pool_size = (2, 2) kernel_size = (3, 3) model = Sequential() # model.add(Convolution2D(32, 3, 3, border_mode='same', input_shape=(64, 64, 1))) # model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1], # border_mode='valid')) # # model.add(Activation('relu')) # model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1])) # model.add(Activation('relu')) # model.add(MaxPooling2D(pool_size=pool_size)) # model.add(Dropout(0.25)) # # model.add(Flatten()) # model.add(Dense(64)) # model.add(Activation('relu')) # model.add(Dropout(0.5)) # model.add(Dense(num_classes)) # model.add(Activation('softmax')) model.add(Convolution2D(32, 3, 3, border_mode='same', input_shape=input_shape)) model.add(Activation('relu')) model.add(Convolution2D(32, 3, 3)) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) # model.add(Dropout(0.25)) model.add(Convolution2D(32, 3, 3, border_mode='same')) model.add(Activation('relu')) model.add(Convolution2D(32, 3, 3)) model.add(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(num_classes)) model.add(Activation('softmax')) # model.summary() model.compile(loss='categorical_crossentropy', optimizer=Adamax(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=1e-5), metrics=["accuracy"]) model.fit(x_train, y_train, batch_size=24, nb_epoch=32, verbose=1, validation_data=(x_test, y_test)) score = model.evaluate(x_test, y_test, verbose=1) return score[1]
def train_cnn1(x_train, y_train, x_test, y_test, num_classes, input_shape): nb_filters = 32 pool_size = (2, 2) kernel_size = (3, 3) model = Sequential() # model.add(Convolution2D(32, 3, 3, border_mode='same', input_shape=(64, 64, 1))) # model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1], # border_mode='valid')) # # model.add(Activation('relu')) # model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1])) # model.add(Activation('relu')) # model.add(MaxPooling2D(pool_size=pool_size)) # model.add(Dropout(0.25)) # # model.add(Flatten()) # model.add(Dense(64)) # model.add(Activation('relu')) # model.add(Dropout(0.5)) # model.add(Dense(num_classes)) # model.add(Activation('softmax')) model.add(Convolution2D(32, 3, 3, border_mode='same', input_shape=input_shape)) model.add(Activation('relu')) model.add(Convolution2D(32, 3, 3)) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) # model.add(Dropout(0.25)) model.add(Convolution2D(32, 3, 3, border_mode='same')) model.add(Activation('relu')) model.add(Convolution2D(32, 3, 3)) model.add(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(num_classes)) model.add(Activation('softmax')) # model.summary() model.compile(loss='categorical_crossentropy', optimizer=Adamax(lr=0.01, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=1e-4), metrics=["accuracy"]) model.fit(x_train, y_train, batch_size=24, nb_epoch=32, verbose=1, validation_data=(x_test, y_test)) score = model.evaluate(x_test, y_test, verbose=1) return score[1]
def lstm_model(self): model = Sequential() first = True for idx in range(len(self.paras.model['hidden_layers'])): if idx == (len(self.paras.model['hidden_layers']) - 1): model.add(LSTM(int(self.paras.model['hidden_layers'][idx]), return_sequences=False)) model.add(Activation(self.paras.model['activation'])) model.add(Dropout(self.paras.model['dropout'])) elif first == True: model.add(LSTM(input_shape=(None, int(self.paras.n_features)), units=int(self.paras.model['hidden_layers'][idx]), return_sequences=True)) model.add(Activation(self.paras.model['activation'])) model.add(Dropout(self.paras.model['dropout'])) first = False else: model.add(LSTM(int(self.paras.model['hidden_layers'][idx]), return_sequences=True)) model.add(Activation(self.paras.model['activation'])) model.add(Dropout(self.paras.model['dropout'])) if self.paras.model['optimizer'] == 'sgd': #optimizer = optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True) optimizer = optimizers.SGD(lr=self.paras.model['learning_rate'], decay=1e-6, momentum=0.9, nesterov=True) elif self.paras.model['optimizer'] == 'rmsprop': #optimizer = optimizers.RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0) optimizer = optimizers.RMSprop(lr=self.paras.model['learning_rate']/10, rho=0.9, epsilon=1e-08, decay=0.0) elif self.paras.model['optimizer'] == 'adagrad': #optimizer = optimizers.Adagrad(lr=0.01, epsilon=1e-08, decay=0.0) optimizer = optimizers.Adagrad(lr=self.paras.model['learning_rate'], epsilon=1e-08, decay=0.0) elif self.paras.model['optimizer'] == 'adam': #optimizer = optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0) optimizer = optimizers.Adam(lr=self.paras.model['learning_rate']/10, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0) elif self.paras.model['optimizer'] == 'adadelta': optimizer = optimizers.Adadelta(lr=1.0, rho=0.95, epsilon=1e-08, decay=0.0) elif self.paras.model['optimizer'] == 'adamax': optimizer = optimizers.Adamax(lr=0.002, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0) elif self.paras.model['optimizer'] == 'nadam': optimizer = optimizers.Nadam(lr=0.002, beta_1=0.9, beta_2=0.999, epsilon=1e-08, schedule_decay=0.004) else: optimizer = optimizers.Adam(lr=self.paras.model['learning_rate']/10, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0) # output layer model.add(Dense(units=self.paras.model['out_layer'])) model.add(Activation(self.paras.model['out_activation'])) model.compile(loss=self.paras.model['loss'], optimizer=optimizer, metrics=['accuracy']) return model
