Python estimator.regression方法代码示例

本文整理汇总了Python中tflearn.layers.estimator.regression方法的典型用法代码示例。如果您正苦于以下问题：Python estimator.regression方法的具体用法？Python estimator.regression怎么用？Python estimator.regression使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tflearn.layers.estimator的用法示例。

在下文中一共展示了estimator.regression方法的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: resnext

# 需要导入模块: from tflearn.layers import estimator [as 别名]
# 或者: from tflearn.layers.estimator import regression [as 别名]
def resnext(width, height, frame_count, lr, output=9, model_name = 'sentnet_color.model'):
    net = input_data(shape=[None, width, height, 3], name='input')
    net = tflearn.conv_2d(net, 16, 3, regularizer='L2', weight_decay=0.0001)
    net = tflearn.layers.conv.resnext_block(net, n, 16, 32)
    net = tflearn.resnext_block(net, 1, 32, 32, downsample=True)
    net = tflearn.resnext_block(net, n-1, 32, 32)
    net = tflearn.resnext_block(net, 1, 64, 32, downsample=True)
    net = tflearn.resnext_block(net, n-1, 64, 32)
    net = tflearn.batch_normalization(net)
    net = tflearn.activation(net, 'relu')
    net = tflearn.global_avg_pool(net)
    # Regression
    net = tflearn.fully_connected(net, output, activation='softmax')
    opt = tflearn.Momentum(0.1, lr_decay=0.1, decay_step=32000, staircase=True)
    net = tflearn.regression(net, optimizer=opt,
                             loss='categorical_crossentropy')

    model = tflearn.DNN(net,
                        max_checkpoints=0, tensorboard_verbose=0, tensorboard_dir='log')

    return model

开发者ID:Sentdex，项目名称:pygta5，代码行数:23，代码来源:models.py

示例2: build_network

# 需要导入模块: from tflearn.layers import estimator [as 别名]
# 或者: from tflearn.layers.estimator import regression [as 别名]
def build_network(self):
      print("---> Starting Neural Network") 
      self.network = input_data(shape = [None, 48, 48, 1])
      self.network = conv_2d(self.network, 64, 5, activation = 'relu')
      self.network = max_pool_2d(self.network, 3, strides = 2)
      self.network = conv_2d(self.network, 64, 5, activation = 'relu')
      self.network = max_pool_2d(self.network, 3, strides = 2)
      self.network = conv_2d(self.network, 128, 4, activation = 'relu')
      self.network = dropout(self.network, 0.3)
      self.network = fully_connected(self.network, 3072, activation = 'relu')
      self.network = fully_connected(self.network, len(self.target_classes), activation = 'softmax')
      self.network = regression(self.network,
        optimizer = 'momentum',
        loss = 'categorical_crossentropy')
      self.model = tflearn.DNN(
        self.network,
        checkpoint_path = 'model_1_nimish',
        max_checkpoints = 1,
        tensorboard_verbose = 2
      )
      self.load_model()

开发者ID:nimish1512，项目名称:Emotion-recognition-and-prediction，代码行数:23，代码来源:em_model.py

示例3: get_nn_model

# 需要导入模块: from tflearn.layers import estimator [as 别名]
# 或者: from tflearn.layers.estimator import regression [as 别名]
def get_nn_model(checkpoint_path='nn_motor_model', session=None):
    # Input is a single value (raw motor value)
    network = input_data(shape=[None, 1], name='input')

    # Hidden layer no.1,  
    network = fully_connected(network, 12, activation='linear')
    
    # Output layer
    network = fully_connected(network, 1, activation='tanh')

    # regression
    network = regression(network, loss='mean_square', metric='accuracy', name='target')

    # Verbosity yay nay
    model = tflearn.DNN(network, tensorboard_verbose=3, checkpoint_path=checkpoint_path, session=session)
    return model

开发者ID:kendricktan，项目名称:suiron，代码行数:18，代码来源:SuironML.py

示例4: finalize_get_model

# 需要导入模块: from tflearn.layers import estimator [as 别名]
# 或者: from tflearn.layers.estimator import regression [as 别名]
def finalize_get_model(net, flags):
    net['gap'], curr = dup(global_avg_pool(net['conv_final'], name='gap'))

    net['final'] = regression(curr,
                              optimizer='adam',
                              learning_rate=flags.lr,
                              batch_size=flags.bs,
                              loss='softmax_categorical_crossentropy',
                              name='target',
                              n_classes=flags.nc,
                              shuffle_batches=True)

    model = tflearn.DNN(net['final'],
                        tensorboard_verbose=0,
                        tensorboard_dir=flags.logdir,
                        best_checkpoint_path=os.path.join(flags.logdir,
                                                          flags.run_id,
                                                          flags.run_id),
                        best_val_accuracy=flags.acc_save)

    model.net_dict = net
    model.flags = flags

    return model

开发者ID:daniilidis-group，项目名称:polar-transformer-networks，代码行数:26，代码来源:arch.py

示例5: alexnet

# 需要导入模块: from tflearn.layers import estimator [as 别名]
# 或者: from tflearn.layers.estimator import regression [as 别名]
def alexnet(width, height, lr, output=3):
    network = input_data(shape=[None, width, height, 1], name='input')
    network = conv_2d(network, 96, 11, strides=4, activation='relu')
    network = max_pool_2d(network, 3, strides=2)
    network = local_response_normalization(network)
    network = conv_2d(network, 256, 5, activation='relu')
    network = max_pool_2d(network, 3, strides=2)
    network = local_response_normalization(network)
    network = conv_2d(network, 384, 3, activation='relu')
    network = conv_2d(network, 384, 3, activation='relu')
    network = conv_2d(network, 256, 3, activation='relu')
    network = max_pool_2d(network, 3, strides=2)
    network = local_response_normalization(network)
    network = fully_connected(network, 4096, activation='tanh')
    network = dropout(network, 0.5)
    network = fully_connected(network, 4096, activation='tanh')
    network = dropout(network, 0.5)
    network = fully_connected(network, output, activation='softmax')
    network = regression(network, optimizer='momentum',
                         loss='categorical_crossentropy',
                         learning_rate=lr, name='targets')

    model = tflearn.DNN(network, checkpoint_path='model_alexnet',
                        max_checkpoints=1, tensorboard_verbose=2, tensorboard_dir='log')

    return model

开发者ID:Sentdex，项目名称:pygta5，代码行数:28，代码来源:alexnet.py

示例6: alexnet2

# 需要导入模块: from tflearn.layers import estimator [as 别名]
# 或者: from tflearn.layers.estimator import regression [as 别名]
def alexnet2(width, height, lr, output=3):
    network = input_data(shape=[None, width, height, 1], name='input')
    network = conv_2d(network, 96, 11, strides=4, activation='relu')
    network = max_pool_2d(network, 3, strides=2)
    network = local_response_normalization(network)
    network = conv_2d(network, 256, 5, activation='relu')
    network = max_pool_2d(network, 3, strides=2)
    network = local_response_normalization(network)
    network = conv_2d(network, 384, 3, activation='relu')
    network = conv_2d(network, 384, 3, activation='relu')
    network = conv_2d(network, 256, 3, activation='relu')
    network = max_pool_2d(network, 3, strides=2)
    network = conv_2d(network, 256, 5, activation='relu')
    network = max_pool_2d(network, 3, strides=2)
    network = local_response_normalization(network)
    network = conv_2d(network, 384, 3, activation='relu')
    network = conv_2d(network, 384, 3, activation='relu')
    network = conv_2d(network, 256, 3, activation='relu')
    network = max_pool_2d(network, 3, strides=2)
    network = local_response_normalization(network)
    network = fully_connected(network, 4096, activation='tanh')
    network = dropout(network, 0.5)
    network = fully_connected(network, 4096, activation='tanh')
    network = dropout(network, 0.5)
    network = fully_connected(network, 4096, activation='tanh')
    network = dropout(network, 0.5)
    network = fully_connected(network, 4096, activation='tanh')
    network = dropout(network, 0.5)
    network = fully_connected(network, output, activation='softmax')
    network = regression(network, optimizer='momentum',
                         loss='categorical_crossentropy',
                         learning_rate=lr, name='targets')

    model = tflearn.DNN(network, checkpoint_path='model_alexnet',
                        max_checkpoints=1, tensorboard_verbose=2, tensorboard_dir='log')

    return model

开发者ID:Sentdex，项目名称:pygta5，代码行数:39，代码来源:alexnet.py