Python tflearn.layers.conv.max_pool_2d() Examples

The following are 24 code examples of tflearn.layers.conv.max_pool_2d(). 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.conv , or try the search function .
Example #1
Source File: inceptionVxOnFire.py    From fire-detection-cnn with MIT License 6 votes vote down vote up 
def reduction_block_b(reduction_input_b):

    reduction_b_1_1 = conv_2d(reduction_input_b,192,1,activation='relu',name='reduction_b_1_1')
    reduction_b_1_3 = conv_2d(reduction_b_1_1,192,3,strides=2,padding='valid',name='reduction_b_1_3')

    reduction_b_3_3_reduce = conv_2d(reduction_input_b, 256, filter_size=1, activation='relu', name='reduction_b_3_3_reduce')
    reduction_b_3_3_asym_1 = conv_2d(reduction_b_3_3_reduce, 256, filter_size=[1,7],  activation='relu',name='reduction_b_3_3_asym_1')
    reduction_b_3_3_asym_2 = conv_2d(reduction_b_3_3_asym_1, 320, filter_size=[7,1],  activation='relu',name='reduction_b_3_3_asym_2')
    reduction_b_3_3=conv_2d(reduction_b_3_3_asym_2,320,3,strides=2,activation='relu',padding='valid',name='reduction_b_3_3')

    reduction_b_pool = max_pool_2d(reduction_input_b,kernel_size=3,strides=2,padding='valid')

    # merge the reduction_b

    reduction_b_output = merge([reduction_b_1_3,reduction_b_3_3,reduction_b_pool],mode='concat',axis=3)

    return reduction_b_output

################################################################################

# InceptionV4 : defintion of inception_block_c
Example #2
Source File: inceptionVxOnFire.py    From fire-detection-cnn with MIT License 6 votes vote down vote up 
def reduction_block_a(reduction_input_a):

    reduction_a_conv1_1_1 = conv_2d(reduction_input_a,384,3,strides=2,padding='valid',activation='relu',name='reduction_a_conv1_1_1')

    reduction_a_conv2_1_1 = conv_2d(reduction_input_a,192,1,activation='relu',name='reduction_a_conv2_1_1')
    reduction_a_conv2_3_3 = conv_2d(reduction_a_conv2_1_1,224,3,activation='relu',name='reduction_a_conv2_3_3')
    reduction_a_conv2_3_3_s2 = conv_2d(reduction_a_conv2_3_3,256,3,strides=2,padding='valid',activation='relu',name='reduction_a_conv2_3_3_s2')

    reduction_a_pool = max_pool_2d(reduction_input_a,strides=2,padding='valid',kernel_size=3,name='reduction_a_pool')

    # merge reduction_a

    reduction_a = merge([reduction_a_conv1_1_1,reduction_a_conv2_3_3_s2,reduction_a_pool],mode='concat',axis=3)

    return reduction_a

################################################################################

# InceptionV4 : definition of inception_block_b
Example #3
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 #4
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 #5
Source File: single_layer_network.py    From DeepOSM with MIT License 5 votes vote down vote up 
def model_for_type(neural_net_type, tile_size, on_band_count):
    """The neural_net_type can be: one_layer_relu,
                                   one_layer_relu_conv,
                                   two_layer_relu_conv."""
    network = tflearn.input_data(shape=[None, tile_size, tile_size, on_band_count])

    # NN architectures mirror ch. 3 of www.cs.toronto.edu/~vmnih/docs/Mnih_Volodymyr_PhD_Thesis.pdf
    if neural_net_type == 'one_layer_relu':
        network = tflearn.fully_connected(network, 64, activation='relu')
    elif neural_net_type == 'one_layer_relu_conv':
        network = conv_2d(network, 64, 12, strides=4, activation='relu')
        network = max_pool_2d(network, 3)
    elif neural_net_type == 'two_layer_relu_conv':
        network = conv_2d(network, 64, 12, strides=4, activation='relu')
        network = max_pool_2d(network, 3)
        network = conv_2d(network, 128, 4, activation='relu')
    else:
        print("ERROR: exiting, unknown layer type for neural net")

    # classify as road or not road
    softmax = tflearn.fully_connected(network, 2, activation='softmax')

    # hyperparameters based on www.cs.toronto.edu/~vmnih/docs/Mnih_Volodymyr_PhD_Thesis.pdf
    momentum = tflearn.optimizers.Momentum(
        learning_rate=.005, momentum=0.9,
        lr_decay=0.0002, name='Momentum')

    net = tflearn.regression(softmax, optimizer=momentum, loss='categorical_crossentropy')

    return tflearn.DNN(net, tensorboard_verbose=0)
Example #6
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 #7
Source File: weights_loading_scope.py    From FRU with MIT License 5 votes vote down vote up 
def make_core_network(network):
        network = tflearn.reshape(network, [-1, 28, 28, 1], name="reshape")
        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')
        return network
Example #8
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 #9
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 #10
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 #11
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 #12
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 #13
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 #14
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 #15
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 #16
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 #17
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 #18
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 #19
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 #20
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 #21
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 #22
Source File: model.py    From facial-expression-recognition-using-cnn with GNU General Public License v3.0 4 votes vote down vote up 
def build_modelB(optimizer=HYPERPARAMS.optimizer, optimizer_param=HYPERPARAMS.optimizer_param, 
    learning_rate=HYPERPARAMS.learning_rate, keep_prob=HYPERPARAMS.keep_prob,
    learning_rate_decay=HYPERPARAMS.learning_rate_decay, decay_step=HYPERPARAMS.decay_step):

    images_network = input_data(shape=[None, NETWORK.input_size, NETWORK.input_size, 1], name='input1')
    images_network = conv_2d(images_network, 64, 3, activation=NETWORK.activation)
    #images_network = local_response_normalization(images_network)
    if NETWORK.use_batchnorm_after_conv_layers:
        images_network = batch_normalization(images_network)
    images_network = max_pool_2d(images_network, 3, strides = 2)
    images_network = conv_2d(images_network, 128, 3, activation=NETWORK.activation)
    if NETWORK.use_batchnorm_after_conv_layers:
        images_network = batch_normalization(images_network)
    images_network = max_pool_2d(images_network, 3, strides = 2)
    images_network = conv_2d(images_network, 256, 3, activation=NETWORK.activation)
    if NETWORK.use_batchnorm_after_conv_layers:
        images_network = batch_normalization(images_network)
    images_network = max_pool_2d(images_network, 3, strides = 2)
    images_network = dropout(images_network, keep_prob=keep_prob)
    images_network = fully_connected(images_network, 4096, activation=NETWORK.activation)
    images_network = dropout(images_network, keep_prob=keep_prob)
    images_network = fully_connected(images_network, 1024, activation=NETWORK.activation)
    if NETWORK.use_batchnorm_after_fully_connected_layers:
        images_network = batch_normalization(images_network)

    if NETWORK.use_landmarks or NETWORK.use_hog_and_landmarks:
        if NETWORK.use_hog_sliding_window_and_landmarks:
            landmarks_network = input_data(shape=[None, 2728], name='input2')
        elif NETWORK.use_hog_and_landmarks:
            landmarks_network = input_data(shape=[None, 208], name='input2')
        else:
            landmarks_network = input_data(shape=[None, 68, 2], name='input2')
        landmarks_network = fully_connected(landmarks_network, 1024, activation=NETWORK.activation)
        if NETWORK.use_batchnorm_after_fully_connected_layers:
            landmarks_network = batch_normalization(landmarks_network)
        landmarks_network = fully_connected(landmarks_network, 128, activation=NETWORK.activation)
        if NETWORK.use_batchnorm_after_fully_connected_layers:
            landmarks_network = batch_normalization(landmarks_network)
        images_network = fully_connected(images_network, 128, activation=NETWORK.activation)
        network = merge([images_network, landmarks_network], 'concat', axis=1)
    else:
        network = images_network
    network = fully_connected(network, NETWORK.output_size, activation='softmax')

    if optimizer == 'momentum':
        optimizer = Momentum(learning_rate=learning_rate, momentum=optimizer_param, 
                    lr_decay=learning_rate_decay, decay_step=decay_step)
    elif optimizer == 'adam':
        optimizer = Adam(learning_rate=learning_rate, beta1=optimizer_param, beta2=learning_rate_decay)
    else:
        print( "Unknown optimizer: {}".format(optimizer))
    network = regression(network, optimizer=optimizer, loss=NETWORK.loss, learning_rate=learning_rate, name='output')

    return network
Example #23
Source File: model.py    From facial-expression-recognition-using-cnn with GNU General Public License v3.0 4 votes vote down vote up 
def build_modelA(optimizer=HYPERPARAMS.optimizer, optimizer_param=HYPERPARAMS.optimizer_param, 
    learning_rate=HYPERPARAMS.learning_rate, keep_prob=HYPERPARAMS.keep_prob,
    learning_rate_decay=HYPERPARAMS.learning_rate_decay, decay_step=HYPERPARAMS.decay_step):

    images_network = input_data(shape=[None, NETWORK.input_size, NETWORK.input_size, 1], name='input1')
    images_network = conv_2d(images_network, 64, 5, activation=NETWORK.activation)
    #images_network = local_response_normalization(images_network)
    if NETWORK.use_batchnorm_after_conv_layers:
        images_network = batch_normalization(images_network)
    images_network = max_pool_2d(images_network, 3, strides = 2)
    images_network = conv_2d(images_network, 64, 5, activation=NETWORK.activation)
    if NETWORK.use_batchnorm_after_conv_layers:
        images_network = batch_normalization(images_network)
    images_network = max_pool_2d(images_network, 3, strides = 2)
    images_network = conv_2d(images_network, 128, 4, activation=NETWORK.activation)
    if NETWORK.use_batchnorm_after_conv_layers:
        images_network = batch_normalization(images_network)
    images_network = dropout(images_network, keep_prob=keep_prob)
    images_network = fully_connected(images_network, 1024, activation=NETWORK.activation)
    if NETWORK.use_batchnorm_after_fully_connected_layers:
        images_network = batch_normalization(images_network)

    if NETWORK.use_landmarks or NETWORK.use_hog_and_landmarks:
        if NETWORK.use_hog_sliding_window_and_landmarks:
            landmarks_network = input_data(shape=[None, 2728], name='input2')
        elif NETWORK.use_hog_and_landmarks:
            landmarks_network = input_data(shape=[None, 208], name='input2')
        else:
            landmarks_network = input_data(shape=[None, 68, 2], name='input2')
        landmarks_network = fully_connected(landmarks_network, 1024, activation=NETWORK.activation)
        if NETWORK.use_batchnorm_after_fully_connected_layers:
            landmarks_network = batch_normalization(landmarks_network)
        landmarks_network = fully_connected(landmarks_network, 40, activation=NETWORK.activation)
        if NETWORK.use_batchnorm_after_fully_connected_layers:
            landmarks_network = batch_normalization(landmarks_network)
        images_network = fully_connected(images_network, 40, activation=NETWORK.activation)
        network = merge([images_network, landmarks_network], 'concat', axis=1)
    else:
        network = images_network
    network = fully_connected(network, NETWORK.output_size, activation='softmax')

    if optimizer == 'momentum':
        optimizer = Momentum(learning_rate=learning_rate, momentum=optimizer_param, 
                    lr_decay=learning_rate_decay, decay_step=decay_step)
    elif optimizer == 'adam':
        optimizer = Adam(learning_rate=learning_rate, beta1=optimizer_param, beta2=learning_rate_decay)
    else:
        print( "Unknown optimizer: {}".format(optimizer))
    network = regression(network, optimizer=optimizer, loss=NETWORK.loss, learning_rate=learning_rate, name='output')

    return network
Example #24
Source File: inceptionVxOnFire.py    From fire-detection-cnn with MIT License 4 votes vote down vote up 
def construct_inceptionv4onfire(x,y, training=True, enable_batch_norm=True):

    network = input_data(shape=[None, y, x, 3])

    #stem of inceptionV4

    conv1_3_3 = conv_2d(network,32,3,strides=2,activation='relu',name='conv1_3_3_s2',padding='valid')
    conv2_3_3 = conv_2d(conv1_3_3,32,3,activation='relu',name='conv2_3_3')
    conv3_3_3 = conv_2d(conv2_3_3,64,3,activation='relu',name='conv3_3_3')
    b_conv_1_pool = max_pool_2d(conv3_3_3,kernel_size=3,strides=2,padding='valid',name='b_conv_1_pool')
    if enable_batch_norm:
        b_conv_1_pool = batch_normalization(b_conv_1_pool)
    b_conv_1_conv = conv_2d(conv3_3_3,96,3,strides=2,padding='valid',activation='relu',name='b_conv_1_conv')
    b_conv_1 = merge([b_conv_1_conv,b_conv_1_pool],mode='concat',axis=3)

    b_conv4_1_1 = conv_2d(b_conv_1,64,1,activation='relu',name='conv4_3_3')
    b_conv4_3_3 = conv_2d(b_conv4_1_1,96,3,padding='valid',activation='relu',name='conv5_3_3')

    b_conv4_1_1_reduce = conv_2d(b_conv_1,64,1,activation='relu',name='b_conv4_1_1_reduce')
    b_conv4_1_7 = conv_2d(b_conv4_1_1_reduce,64,[1,7],activation='relu',name='b_conv4_1_7')
    b_conv4_7_1 = conv_2d(b_conv4_1_7,64,[7,1],activation='relu',name='b_conv4_7_1')
    b_conv4_3_3_v = conv_2d(b_conv4_7_1,96,3,padding='valid',name='b_conv4_3_3_v')
    b_conv_4 = merge([b_conv4_3_3_v, b_conv4_3_3],mode='concat',axis=3)

    b_conv5_3_3 = conv_2d(b_conv_4,192,3,padding='valid',activation='relu',name='b_conv5_3_3',strides=2)
    b_pool5_3_3 = max_pool_2d(b_conv_4,kernel_size=3,padding='valid',strides=2,name='b_pool5_3_3')
    if enable_batch_norm:
        b_pool5_3_3 = batch_normalization(b_pool5_3_3)
    b_conv_5 = merge([b_conv5_3_3,b_pool5_3_3],mode='concat',axis=3)
    net = b_conv_5

    # inceptionV4 modules

    net=inception_block_a(net)

    net=inception_block_b(net)

    net=inception_block_c(net)

    pool5_7_7=global_avg_pool(net)
    if(training):
        pool5_7_7=dropout(pool5_7_7,0.4)
    loss = fully_connected(pool5_7_7, 2,activation='softmax')

    if(training):
        network = regression(loss, optimizer='rmsprop',
                             loss='categorical_crossentropy',
                             learning_rate=0.001)
    else:
        network=loss

    model = tflearn.DNN(network, checkpoint_path='inceptionv4onfire',
                        max_checkpoints=1, tensorboard_verbose=0)

    return model

################################################################################