Python tflearn.layers.estimator.regression() Examples

The following are 30 code examples of tflearn.layers.estimator.regression(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module tflearn.layers.estimator , or try the search function .
Example #1
Source File: models.py    From pygta5 with GNU General Public License v3.0 8 votes vote down vote up 
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
Example #2
Source File: arch.py    From polar-transformer-networks with MIT License 6 votes vote down vote up 
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
Example #3
Source File: SuironML.py    From suiron with MIT License 6 votes vote down vote up 
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
Example #4
Source File: models.py    From pygta5 with GNU General Public License v3.0 6 votes vote down vote up 
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
Example #5
Source File: models.py    From pygta5 with GNU General Public License v3.0 6 votes vote down vote up 
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
Example #6
Source File: em_model.py    From Emotion-recognition-and-prediction with Apache License 2.0 6 votes vote down vote up 
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()
Example #7
Source File: models.py    From pygta5 with GNU General Public License v3.0 6 votes vote down vote up 
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
Example #8
Source File: models.py    From pygta5 with GNU General Public License v3.0 5 votes vote down vote up 
def sentnet(width, height, frame_count, lr, output=9):
    network = input_data(shape=[None, width, height, frame_count, 1], name='input')
    network = conv_3d(network, 96, 11, strides=4, activation='relu')
    network = avg_pool_3d(network, 3, strides=2)
    #network = local_response_normalization(network)
    network = conv_3d(network, 256, 5, activation='relu')
    network = avg_pool_3d(network, 3, strides=2)
    #network = local_response_normalization(network)
    network = conv_3d(network, 384, 3, activation='relu')
    network = conv_3d(network, 384, 3, activation='relu')
    network = conv_3d(network, 256, 3, activation='relu')
    network = max_pool_3d(network, 3, strides=2)
    network = conv_3d(network, 256, 5, activation='relu')
    network = avg_pool_3d(network, 3, strides=2)
    #network = local_response_normalization(network)
    network = conv_3d(network, 384, 3, activation='relu')
    network = conv_3d(network, 384, 3, activation='relu')
    network = conv_3d(network, 256, 3, activation='relu')
    network = avg_pool_3d(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=0, tensorboard_dir='log')

    return model
Example #9
Source File: models.py    From pygta5 with GNU General Public License v3.0 5 votes vote down vote up 
def sentnet2(width, height, frame_count, lr, output=9):
    network = input_data(shape=[None, width, height, frame_count, 1], name='input')
    network = conv_3d(network, 96, 11, strides=4, activation='relu')
    network = max_pool_3d(network, 3, strides=2)
    #network = local_response_normalization(network)
    network = conv_3d(network, 256, 5, activation='relu')
    network = max_pool_3d(network, 3, strides=2)
    #network = local_response_normalization(network)
    network = conv_3d(network, 384, 3, activation='relu')
    network = conv_3d(network, 384, 3, activation='relu')
    network = conv_3d(network, 256, 3, activation='relu')
    network = max_pool_3d(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, 3, 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=0, tensorboard_dir='log')

    return model
Example #10
Source File: models.py    From pygta5 with GNU General Public License v3.0 5 votes vote down vote up 
def sentnet_v0(width, height, frame_count, lr, output=9):
    network = input_data(shape=[None, width, height, frame_count, 1], name='input')
    network = conv_3d(network, 96, 11, strides=4, activation='relu')
    network = max_pool_3d(network, 3, strides=2)
    
    #network = local_response_normalization(network)
    
    network = conv_3d(network, 256, 5, activation='relu')
    network = max_pool_3d(network, 3, strides=2)

    #network = local_response_normalization(network)
    
    network = conv_3d(network, 384, 3, 3, activation='relu')
    network = conv_3d(network, 384, 3, 3, activation='relu')
    network = conv_3d(network, 256, 3, 3, activation='relu')

    network = max_pool_3d(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=0, tensorboard_dir='log')

    return model
Example #11
Source File: models.py    From pygta5 with GNU General Public License v3.0 5 votes vote down vote up 
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=0, tensorboard_dir='log')

    return model
Example #12
Source File: models.py    From pygta5 with GNU General Public License v3.0 5 votes vote down vote up 
def sentnet_color_2d(width, height, frame_count, lr, output=9, model_name = 'sentnet_color.model'):
    network = input_data(shape=[None, width, height, 3], 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,
                        max_checkpoints=0, tensorboard_verbose=0, tensorboard_dir='log')

    return model
Example #13
Source File: models.py    From pygta5 with GNU General Public License v3.0 5 votes vote down vote up 
def sentnet_LSTM_gray(width, height, frame_count, lr, output=9):
    network = input_data(shape=[None, width, height], name='input')
    #network = tflearn.input_data(shape=[None, 28, 28], name='input')
    network = tflearn.lstm(network, 128, return_seq=True)
    network = tflearn.lstm(network, 128)
    network = tflearn.fully_connected(network, 9, activation='softmax')
    network = tflearn.regression(network, optimizer='adam',
    loss='categorical_crossentropy', name="output1")

    model = tflearn.DNN(network, checkpoint_path='model_lstm',
                        max_checkpoints=1, tensorboard_verbose=0, tensorboard_dir='log')

    return model
Example #14
Source File: models.py    From pygta5 with GNU General Public License v3.0 5 votes vote down vote up 
def sentnet_frames(width, height, frame_count, lr, output=9):
    network = input_data(shape=[None, width, height,frame_count, 1], name='input')
    network = conv_3d(network, 96, 11, strides=4, activation='relu')
    network = max_pool_3d(network, 3, strides=2)
    #network = local_response_normalization(network)
    network = conv_3d(network, 256, 5, activation='relu')
    network = max_pool_3d(network, 3, strides=2)
    #network = local_response_normalization(network)
    network = conv_3d(network, 384, 3, activation='relu')
    network = conv_3d(network, 384, 3, activation='relu')
    network = conv_3d(network, 256, 3, activation='relu')
    network = max_pool_3d(network, 3, strides=2)
    network = conv_3d(network, 256, 5, activation='relu')
    network = max_pool_3d(network, 3, strides=2)
    #network = local_response_normalization(network)
    network = conv_3d(network, 384, 3, activation='relu')
    network = conv_3d(network, 384, 3, activation='relu')
    network = conv_3d(network, 256, 3, activation='relu')
    network = max_pool_3d(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=0, tensorboard_dir='log')

    return model
Example #15
Source File: models.py    From pygta5 with GNU General Public License v3.0 5 votes vote down vote up 
def sentnet2(width, height, frame_count, lr, output=9):
    network = input_data(shape=[None, width, height, frame_count, 1], name='input')
    network = conv_3d(network, 96, 11, strides=4, activation='relu')
    network = max_pool_3d(network, 3, strides=2)
    #network = local_response_normalization(network)
    network = conv_3d(network, 256, 5, activation='relu')
    network = max_pool_3d(network, 3, strides=2)
    #network = local_response_normalization(network)
    network = conv_3d(network, 384, 3, activation='relu')
    network = conv_3d(network, 384, 3, activation='relu')
    network = conv_3d(network, 256, 3, activation='relu')
    network = max_pool_3d(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, 3, 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=0, tensorboard_dir='log')

    return model
Example #16
Source File: models.py    From pygta5 with GNU General Public License v3.0 5 votes vote down vote up 
def sentnet(width, height, frame_count, lr, output=9):
    network = input_data(shape=[None, width, height, frame_count, 1], name='input')
    network = conv_3d(network, 96, 11, strides=4, activation='relu')
    network = avg_pool_3d(network, 3, strides=2)
    #network = local_response_normalization(network)
    network = conv_3d(network, 256, 5, activation='relu')
    network = avg_pool_3d(network, 3, strides=2)
    #network = local_response_normalization(network)
    network = conv_3d(network, 384, 3, activation='relu')
    network = conv_3d(network, 384, 3, activation='relu')
    network = conv_3d(network, 256, 3, activation='relu')
    network = max_pool_3d(network, 3, strides=2)
    network = conv_3d(network, 256, 5, activation='relu')
    network = avg_pool_3d(network, 3, strides=2)
    #network = local_response_normalization(network)
    network = conv_3d(network, 384, 3, activation='relu')
    network = conv_3d(network, 384, 3, activation='relu')
    network = conv_3d(network, 256, 3, activation='relu')
    network = avg_pool_3d(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=0, tensorboard_dir='log')

    return model
Example #17
Source File: models.py    From pygta5 with GNU General Public License v3.0 5 votes vote down vote up 
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=0, tensorboard_dir='log')

    return model
Example #18
Source File: models.py    From pygta5 with GNU General Public License v3.0 5 votes vote down vote up 
def sentnet_v0(width, height, frame_count, lr, output=9):
    network = input_data(shape=[None, width, height, frame_count, 1], name='input')
    network = conv_3d(network, 96, 11, strides=4, activation='relu')
    network = max_pool_3d(network, 3, strides=2)
    
    #network = local_response_normalization(network)
    
    network = conv_3d(network, 256, 5, activation='relu')
    network = max_pool_3d(network, 3, strides=2)

    #network = local_response_normalization(network)
    
    network = conv_3d(network, 384, 3, 3, activation='relu')
    network = conv_3d(network, 384, 3, 3, activation='relu')
    network = conv_3d(network, 256, 3, 3, activation='relu')

    network = max_pool_3d(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=0, tensorboard_dir='log')

    return model
Example #19
Source File: alexnet.py    From pygta5 with GNU General Public License v3.0 5 votes vote down vote up 
def alexnet(width, height, lr):
    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, 3, 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=0, tensorboard_dir='log')

    return model
Example #20
Source File: alexnet.py    From pygta5 with GNU General Public License v3.0 5 votes vote down vote up 
def alexnet(width, height, lr):
    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, 3, 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
Example #21
Source File: weights_loading_scope.py    From FRU with MIT License 5 votes vote down vote up 
def __init__(self):
        network = tflearn.input_data(shape=[None, 784], name="input")
        network = self.make_core_network(network)
        network = regression(network, optimizer='adam', learning_rate=0.01,
                             loss='categorical_crossentropy', name='target')
        
        model = tflearn.DNN(network, tensorboard_verbose=0)
        self.model = model
Example #22
Source File: weights_loading_scope.py    From FRU with MIT License 5 votes vote down vote up 
def __init__(self):
        # Building deep neural network
        network = tflearn.input_data(shape=[None, 784], name="input")
        network = self.make_core_network(network)

        # Regression using SGD with learning rate decay and Top-3 accuracy
        sgd = tflearn.SGD(learning_rate=0.1, lr_decay=0.96, decay_step=1000)
        top_k = tflearn.metrics.Top_k(3)

        network = tflearn.regression(network, optimizer=sgd, metric=top_k,
                                 loss='categorical_crossentropy', name="target")
        model = tflearn.DNN(network, tensorboard_verbose=0)
        self.model = model
Example #23
Source File: weights_loading_scope.py    From FRU with MIT License 5 votes vote down vote up 
def __init__(self):
        inputs = tflearn.input_data(shape=[None, 784], name="input")

        with tf.variable_scope("scope1") as scope:
            net_conv = Model1.make_core_network(inputs)	# shape (?, 10)
        with tf.variable_scope("scope2") as scope:
            net_dnn = Model2.make_core_network(inputs)	# shape (?, 10)

        network = tf.concat([net_conv, net_dnn], 1, name="concat")	# shape (?, 20)
        network = tflearn.fully_connected(network, 10, activation="softmax")
        network = regression(network, optimizer='adam', learning_rate=0.01,
                             loss='categorical_crossentropy', name='target')

        self.model = tflearn.DNN(network, tensorboard_verbose=0)
Example #24
Source File: test_validation_monitors.py    From FRU with MIT License 5 votes vote down vote up 
def test_vbs1(self):

        with tf.Graph().as_default():
            # Data loading and preprocessing
            import tflearn.datasets.mnist as mnist
            X, Y, testX, testY = mnist.load_data(one_hot=True)
            X = X.reshape([-1, 28, 28, 1])
            testX = testX.reshape([-1, 28, 28, 1])
            X = X[:20, :, :, :]
            Y = Y[:20, :]
            testX = testX[:10, :, :, :]
            testY = testY[:10, :]
            
            # Building convolutional network
            network = input_data(shape=[None, 28, 28, 1], name='input')
            network = conv_2d(network, 32, 3, activation='relu', regularizer="L2")
            network = max_pool_2d(network, 2)
            network = local_response_normalization(network)
            network = conv_2d(network, 64, 3, activation='relu', regularizer="L2")
            network = max_pool_2d(network, 2)
            network = local_response_normalization(network)
            network = fully_connected(network, 128, activation='tanh')
            network = dropout(network, 0.8)
            network = fully_connected(network, 256, activation='tanh')
            network = dropout(network, 0.8)
            network = fully_connected(network, 10, activation='softmax')
            network = regression(network, optimizer='adam', learning_rate=0.01,
                                 loss='categorical_crossentropy', name='target')
            
            # Training
            model = tflearn.DNN(network, tensorboard_verbose=3)
            model.fit({'input': X}, {'target': Y}, n_epoch=1,
                      batch_size=10,
                      validation_set=({'input': testX}, {'target': testY}),
                      validation_batch_size=5,
                      snapshot_step=10, show_metric=True, run_id='convnet_mnist_vbs')
    
            self.assertEqual(model.train_ops[0].validation_batch_size, 5)
            self.assertEqual(model.train_ops[0].batch_size, 10)
Example #25
Source File: emotion_recognition.py    From emotion-recognition-neural-networks with MIT License 5 votes vote down vote up 
def build_network(self):
        # Smaller 'AlexNet'
        # https://github.com/tflearn/tflearn/blob/master/examples/images/alexnet.py
        print('[+] Building CNN')
        self.network = input_data(shape=[None, SIZE_FACE, SIZE_FACE, 1])
        self.network = conv_2d(self.network, 64, 5, activation='relu')
        #self.network = local_response_normalization(self.network)
        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(EMOTIONS), activation='softmax')
        self.network = regression(
            self.network,
            optimizer='momentum',
            loss='categorical_crossentropy'
        )
        self.model = tflearn.DNN(
            self.network,
            checkpoint_path=SAVE_DIRECTORY + '/emotion_recognition',
            max_checkpoints=1,
            tensorboard_verbose=2
        )
        self.load_model()
Example #26
Source File: models.py    From pygta5 with GNU General Public License v3.0 5 votes vote down vote up 
def sentnet_frames(width, height, frame_count, lr, output=9):
    network = input_data(shape=[None, width, height,frame_count, 1], name='input')
    network = conv_3d(network, 96, 11, strides=4, activation='relu')
    network = max_pool_3d(network, 3, strides=2)
    #network = local_response_normalization(network)
    network = conv_3d(network, 256, 5, activation='relu')
    network = max_pool_3d(network, 3, strides=2)
    #network = local_response_normalization(network)
    network = conv_3d(network, 384, 3, activation='relu')
    network = conv_3d(network, 384, 3, activation='relu')
    network = conv_3d(network, 256, 3, activation='relu')
    network = max_pool_3d(network, 3, strides=2)
    network = conv_3d(network, 256, 5, activation='relu')
    network = max_pool_3d(network, 3, strides=2)
    #network = local_response_normalization(network)
    network = conv_3d(network, 384, 3, activation='relu')
    network = conv_3d(network, 384, 3, activation='relu')
    network = conv_3d(network, 256, 3, activation='relu')
    network = max_pool_3d(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=0, tensorboard_dir='log')

    return model
Example #27
Source File: alexnet.py    From pygta5 with GNU General Public License v3.0 5 votes vote down vote up 
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
Example #28
Source File: alexnet.py    From pygta5 with GNU General Public License v3.0 5 votes vote down vote up 
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
Example #29
Source File: models.py    From pygta5 with GNU General Public License v3.0 5 votes vote down vote up 
def sentnet_color_2d(width, height, frame_count, lr, output=9, model_name = 'sentnet_color.model'):
    network = input_data(shape=[None, width, height, 3], 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,
                        max_checkpoints=0, tensorboard_verbose=0, tensorboard_dir='log')

    return model
Example #30
Source File: models.py    From pygta5 with GNU General Public License v3.0 5 votes vote down vote up 
def sentnet_LSTM_gray(width, height, frame_count, lr, output=9):
    network = input_data(shape=[None, width, height], name='input')
    #network = tflearn.input_data(shape=[None, 28, 28], name='input')
    network = tflearn.lstm(network, 128, return_seq=True)
    network = tflearn.lstm(network, 128)
    network = tflearn.fully_connected(network, 9, activation='softmax')
    network = tflearn.regression(network, optimizer='adam',
    loss='categorical_crossentropy', name="output1")

    model = tflearn.DNN(network, checkpoint_path='model_lstm',
                        max_checkpoints=1, tensorboard_verbose=0, tensorboard_dir='log')

    return model