Пример #1
0
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
 def build_network(self):
   # Building 'AlexNet'
   # https://github.com/tflearn/tflearn/blob/master/examples/images/alexnet.py
   # https://github.com/DT42/squeezenet_demo
   # https://github.com/yhenon/pysqueezenet/blob/master/squeezenet.py
   print('[+] Building CNN')
   self.network = input_data(shape = [None, SIZE_FACE, SIZE_FACE, 1])
   self.network = conv_2d(self.network, 96, 11, strides = 4, activation = 'relu')
   self.network = max_pool_2d(self.network, 3, strides = 2)
   self.network = local_response_normalization(self.network)
   self.network = conv_2d(self.network, 256, 5, activation = 'relu')
   self.network = max_pool_2d(self.network, 3, strides = 2)
   self.network = local_response_normalization(self.network)
   self.network = conv_2d(self.network, 256, 3, activation = 'relu')
   self.network = max_pool_2d(self.network, 3, strides = 2)
   self.network = local_response_normalization(self.network)
   self.network = fully_connected(self.network, 1024, activation = 'tanh')
   self.network = dropout(self.network, 0.5)
   self.network = fully_connected(self.network, 1024, activation = 'tanh')
   self.network = dropout(self.network, 0.5)
   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 + '/alexnet_mood_recognition',
     max_checkpoints = 1,
     tensorboard_verbose = 2
   )
   self.load_model()
Пример #3
0
def train_nmf_network(mfcc_array, sdr_array, n_epochs, take):
    """

    :param mfcc_array:
    :param sdr_array:
    :param n_epochs:
    :param take:
    :return:
    """
    with tf.Graph().as_default():
        network = input_data(shape=[None, 13, 100, 1])
        network = conv_2d(network, 32, [5, 5], activation="relu", regularizer="L2")
        network = max_pool_2d(network, 2)
        network = conv_2d(network, 64, [5, 5], activation="relu", regularizer="L2")
        network = max_pool_2d(network, 2)
        network = fully_connected(network, 128, activation="relu")
        network = dropout(network, 0.8)
        network = fully_connected(network, 256, activation="relu")
        network = dropout(network, 0.8)
        network = fully_connected(network, 1, activation="linear")
        regress = tflearn.regression(network, optimizer="rmsprop", loss="mean_square", learning_rate=0.001)

        # Training
        model = tflearn.DNN(regress)  # , session=sess)
        model.fit(
            mfcc_array,
            sdr_array,
            n_epoch=n_epochs,
            snapshot_step=1000,
            show_metric=True,
            run_id="repet_choice_{0}_epochs_take_{1}".format(n_epochs, take),
        )

        return model
def build_model_anything_happening():
    ### IS ANY OF THIS NECESSARY FOR LIGHT/DARK? IN GENERAL W/ STAIONARY CAMERA?
    img_prep = ImagePreprocessing()
    img_prep.add_featurewise_zero_center()
    img_prep.add_featurewise_stdnorm()

    img_aug = ImageAugmentation()
    img_aug.add_random_flip_leftright()

    # Specify shape of the data, image prep
    network = input_data(shape=[None, 52, 64],
                         data_preprocessing=img_prep,
                         data_augmentation=img_aug)

    # Since the image position remains consistent and are fairly similar, this can be spatially aware.
    # Using a fully connected network directly, no need for convolution.
    network = fully_connected(network, 2048, activation='relu')
    network = fully_connected(network, 2, activation='softmax')

    network = regression(network, optimizer='adam',
                         loss='categorical_crossentropy',
                         learning_rate=0.00003)

    model = tflearn.DNN(network, tensorboard_verbose=0)
    return model
Пример #5
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def train_repet_network(beat_spectrum_array, sdr_array, n_epochs, take):
    """

    :param beat_spectrum_array:
    :param sdr_array:
    :param n_epochs:
    :param take:
    :return:
    """
    beat_spec_len = 432
    with tf.Graph().as_default():
        input_layer = input_data(shape=[None, beat_spec_len, 1])
        conv1 = conv_1d(input_layer, 32, 4, activation="relu", regularizer="L2")
        max_pool1 = max_pool_1d(conv1, 2)
        conv2 = conv_1d(max_pool1, 64, 80, activation="relu", regularizer="L2")
        max_pool2 = max_pool_1d(conv2, 2)
        fully1 = fully_connected(max_pool2, 128, activation="relu")
        dropout1 = dropout(fully1, 0.8)
        fully2 = fully_connected(dropout1, 256, activation="relu")
        dropout2 = dropout(fully2, 0.8)
        linear = fully_connected(dropout2, 1, activation="linear")
        regress = tflearn.regression(linear, optimizer="rmsprop", loss="mean_square", learning_rate=0.001)

        # Training
        model = tflearn.DNN(regress)  # , session=sess)
        model.fit(
            beat_spectrum_array,
            sdr_array,
            n_epoch=n_epochs,
            snapshot_step=1000,
            show_metric=True,
            run_id="repet_choice_{0}_epochs_take_{1}".format(n_epochs, take),
        )

        return model
Пример #6
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def createModel(nbClasses,imageSize):
	print("[+] Creating model...")
	convnet = input_data(shape=[None, imageSize, imageSize, 1], name='input')

	convnet = conv_2d(convnet, 64, 2, activation='elu', weights_init="Xavier")
	convnet = max_pool_2d(convnet, 2)

	convnet = conv_2d(convnet, 128, 2, activation='elu', weights_init="Xavier")
	convnet = max_pool_2d(convnet, 2)

	convnet = conv_2d(convnet, 256, 2, activation='elu', weights_init="Xavier")
	convnet = max_pool_2d(convnet, 2)

	convnet = conv_2d(convnet, 512, 2, activation='elu', weights_init="Xavier")
	convnet = max_pool_2d(convnet, 2)

	convnet = fully_connected(convnet, 1024, activation='elu')
	convnet = dropout(convnet, 0.5)

	convnet = fully_connected(convnet, nbClasses, activation='softmax')
	convnet = regression(convnet, optimizer='rmsprop', loss='categorical_crossentropy')

	model = tflearn.DNN(convnet)
	print("    Model created! ✅")
	return model
Пример #7
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def alexnet():
    X, Y = oxflower17.load_data(one_hot=True, resize_pics=(227, 227))

    # Building 'AlexNet'
    network = input_data(shape=[None, 227, 227, 3])
    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, 17, activation='softmax')
    network = regression(network, optimizer='momentum',
                         loss='categorical_crossentropy',
                         learning_rate=0.001)

    # Training
    model = tflearn.DNN(network, checkpoint_path='model_alexnet',
                        max_checkpoints=1, tensorboard_verbose=2)
    model.fit(X, Y, n_epoch=1000, validation_set=0.1, shuffle=True,
              show_metric=True, batch_size=64, snapshot_step=200,
              snapshot_epoch=False, run_id='alexnet')
Пример #8
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def cnn():
    X, Y, testX, testY = mnist.load_data(one_hot=True)
    X = X.reshape([-1, 28, 28, 1])
    testX = testX.reshape([-1, 28, 28, 1])

    # 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=0)
    model.fit({'input': X}, {'target': Y}, n_epoch=20,
               validation_set=({'input': testX}, {'target': testY}),
               snapshot_step=100, show_metric=True, run_id='cnn_demo')
Пример #9
0
def _model1():
    global yTest, img_aug
    tf.reset_default_graph()
    img_prep = ImagePreprocessing()
    img_prep.add_featurewise_zero_center()
    img_prep.add_featurewise_stdnorm()
    network = input_data(shape=[None, inputSize, inputSize, dim],
                 name='input',
                 data_preprocessing=img_prep,
                 data_augmentation=img_aug)

    network = conv_2d(network, 32, 3, strides = 4, activation='relu')
    network = max_pool_2d(network, 2, strides=2)
    network = local_response_normalization(network)
    network = conv_2d(network, 64, 3, strides = 2, activation='relu')
    network = max_pool_2d(network, 2, strides=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, len(Y[0]), activation='softmax')
    network = regression(network, optimizer='adam', learning_rate=0.001,
                 loss='categorical_crossentropy', name='target')

    model = tflearn.DNN(network, tensorboard_verbose=3)
    model.fit(X, Y, n_epoch=epochNum, validation_set=(xTest, yTest),
       snapshot_step=500, show_metric=True, batch_size=batchNum, shuffle=True, run_id=_id + 'artClassification')
    if modelStore: model.save(_id + '-model.tflearn')
Пример #10
0
def do_cnn_doc2vec_2d(trainX, testX, trainY, testY):
    print "CNN and doc2vec 2d"

    trainX = trainX.reshape([-1, max_features, max_document_length, 1])
    testX = testX.reshape([-1, max_features, max_document_length, 1])


    # Building convolutional network
    network = input_data(shape=[None, max_features, max_document_length, 1], name='input')
    network = conv_2d(network, 16, 3, activation='relu', regularizer="L2")
    network = max_pool_2d(network, 2)
    network = local_response_normalization(network)
    network = conv_2d(network, 32, 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=0)
    model.fit({'input': trainX}, {'target': trainY}, n_epoch=20,
               validation_set=({'input': testX}, {'target': testY}),
               snapshot_step=100, show_metric=True, run_id='review')
def main():
    """

    :return:
    """
    # pickle parameters
    fg_or_bg = 'background'
    sdr_type = 'sdr'
    feature = 'sim_mat'
    beat_spec_len = 432

    # set up training, testing, & validation partitions
    sim_mat_array, sdr_array = get_generated_data(feature, fg_or_bg, sdr_type)

    # training params
    n_classes = 10
    n_training_steps = 1000
    training_step_size = 100
    training_percent = 0.85
    testing_percent = 0.15
    validation_percent = 0.00

    sdr_array_1h, hist = sdrs_to_one_hots(sdr_array, n_classes, True)

    train, test, validate = split_into_sets(len(sim_mat_array), training_percent,
                                            testing_percent, validation_percent)

    # Building convolutional network
    network = input_data(shape=[None, beat_spec_len, 1], name='input')
    network = conv_1d(network, 32, 3, activation='relu', regularizer="L2")
    network = max_pool_1d(network, 2)
    # network = local_response_normalization(network)
    # network = batch_normalization(network)
    # network = conv_1d(network, 64, 3, activation='relu', regularizer="L2")
    # network = max_pool_1d(network, 2)
    # network = local_response_normalization(network)
    # network = batch_normalization(network)
    # network = fully_connected(network, 128, activation='tanh')
    # network = dropout(network, 0.5)
    network = fully_connected(network, 512, activation='tanh')
    network = dropout(network, 0.5)
    network = fully_connected(network, n_classes, activation='softmax')
    # network = fully_connected(network, 1, activation='linear')
    network = regression(network, optimizer='adagrad', learning_rate=0.01,
                         loss='categorical_crossentropy', name='target')

    X = np.expand_dims(sim_mat_array, -1)
    Y = np.array(sdr_array_1h)
    # X = np.expand_dims([beat_spec_array[i] for i in train], -1)
    # Y = np.array([sdr_array_1h[i] for i in train])
    # testX = np.expand_dims([beat_spec_array[i] for i in test], -1)
    # testY = np.array([sdr_array[i] for i in test])

    # Training
    model = tflearn.DNN(network, tensorboard_verbose=1)
    model.fit({'input': X}, {'target': Y}, n_epoch=20,
              validation_set=0.1,
              snapshot_step=1000, show_metric=True, run_id='{} classes'.format(n_classes - 1))
def main():
    pickle_folder = '../pickles_rolloff'
    pickle_folders_to_load = [f for f in os.listdir(pickle_folder) if os.path.isdir(join(pickle_folder, f))]
    pickle_folders_to_load = sorted(pickle_folders_to_load)

    # pickle parameters
    fg_or_bg = 'background'
    sdr_type = 'sdr'
    feature = 'sim_mat'
    beat_spec_len = 432

    # training params
    n_classes = 16
    training_percent = 0.85
    testing_percent = 0.15
    validation_percent = 0.00


    # set up training, testing, & validation partitions
    print('Loading sim_mat and sdrs')
    sim_mat_array, sdr_array = get_generated_data(feature, fg_or_bg, sdr_type)
    print('sim_mat and sdrs loaded')

    print('splitting and grooming data')
    train, test, validate = split_into_sets(len(pickle_folders_to_load), training_percent,
                                            testing_percent, validation_percent)

    trainX = np.expand_dims([sim_mat_array[i] for i in train], -1)
    trainY = np.expand_dims([sdr_array[i] for i in train], -1)
    testX = np.expand_dims([sim_mat_array[i] for i in test], -1)
    testY = np.array([sdr_array[i] for i in test])

    print('setting up CNN')
    # Building convolutional network
    network = input_data(shape=[None, beat_spec_len, beat_spec_len, 1])
    network = conv_2d(network, 32, 10, activation='relu', regularizer="L2")
    network = max_pool_2d(network, 2)
    network = conv_2d(network, 64, 20, activation='relu', regularizer="L2")
    network = max_pool_2d(network, 2)
    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, 1, activation='linear')
    regress = tflearn.regression(network, optimizer='sgd', loss='mean_square', learning_rate=0.01)

    print('running CNN')
    # Training
    model = tflearn.DNN(regress, tensorboard_verbose=1)
    model.fit(trainX, trainY, n_epoch=10,
              snapshot_step=1000, show_metric=True, run_id='{} classes'.format(n_classes - 1))

    predicted = np.array(model.predict(testX))[:,0]

    print('plotting')
    plot(testY, predicted)
Пример #13
0
def main():
    pickle_folder = '../pickles_rolloff'
    pickle_folders_to_load = [f for f in os.listdir(pickle_folder) if os.path.isdir(join(pickle_folder, f))]
    pickle_folders_to_load = sorted(pickle_folders_to_load)

    # pickle parameters
    fg_or_bg = 'background'
    sdr_type = 'sdr'
    feature = 'beat_spec'
    beat_spec_len = 432

    # training params
    n_classes = 16
    training_percent = 0.85
    testing_percent = 0.15
    validation_percent = 0.00


    # set up training, testing, & validation partitions
    beat_spec_array, sdr_array = load_beat_spec_and_sdrs(pickle_folders_to_load, pickle_folder,
                                                         feature, fg_or_bg, sdr_type)

    train, test, validate = split_into_sets(len(pickle_folders_to_load), training_percent,
                                            testing_percent, validation_percent)

    trainX = np.expand_dims([beat_spec_array[i] for i in train], -1)
    trainY = np.expand_dims([sdr_array[i] for i in train], -1)
    testX = np.expand_dims([beat_spec_array[i] for i in test], -1)
    testY = np.array([sdr_array[i] for i in test])

    # Building convolutional network
    network = input_data(shape=[None, beat_spec_len, 1])
    network = conv_1d(network, 32, 4, activation='relu', regularizer="L2")
    network = max_pool_1d(network, 2)
    network = conv_1d(network, 64, 80, activation='relu', regularizer="L2")
    network = max_pool_1d(network, 2)
    network = fully_connected(network, 128, activation='relu')
    network = dropout(network, 0.8)
    network = fully_connected(network, 256, activation='relu') # look for non-tanh things???
    network = dropout(network, 0.8)
    network = fully_connected(network, 1, activation='linear')
    regress = tflearn.regression(network, optimizer='sgd', loss='mean_square', learning_rate=0.01)

    # Training
    model = tflearn.DNN(regress, tensorboard_verbose=1)
    model.fit(trainX, trainY, n_epoch=100,
              snapshot_step=1000, show_metric=True, run_id='relus_100_3')

    predicted = np.array(model.predict(testX))[:,0]
    # pprint.pprint()
    print("Test MSE: ", np.square(testY - predicted).mean())
    plot(testY, predicted)
Пример #14
0
def build_model_1_conv(learning_rate, input_shape, nb_classes, base_path , drop):
    network = input_data(shape=input_shape, name='input')
    network = conv_2d(network, 64, [4, 16], activation='relu')
    network = fully_connected(network, 128, activation='relu')
    network = dropout(network, drop)
    network = fully_connected(network, 64, activation='relu')
    network = dropout(network, drop)
    network = fully_connected(network, nb_classes, activation='softmax')
    network = regression(network, optimizer='sgd', learning_rate=learning_rate,
                         loss='categorical_crossentropy', name='target')
    model = tflearn.DNN(network, tensorboard_verbose=3, tensorboard_dir=base_path + "/tflearn_logs/",
                        checkpoint_path=base_path + "/checkpoints/step")
    return model
def main():
    pickle_folder = 'pickles_combined'


    # pickle parameters
    fg_or_bg = 'background'
    sdr_type = 'sdr'
    feature = 'beat_spec'

    # training params
    training_percent = 0.85
    testing_percent = 0.15
    validation_percent = 0.00
    beat_spec_max = 355


    # set up training, testing, & validation partitions
    beat_spec_array, sdr_array = unpickle_beat_spec_and_sdrs(pickle_folder, beat_spec_max)

    train, test, validate = split_into_sets(len(beat_spec_array), training_percent,
                                            testing_percent, validation_percent)

    trainX = np.expand_dims([beat_spec_array[i] for i in train], -1)
    trainY = np.expand_dims([sdr_array[i] for i in train], -1)
    testX = np.expand_dims([beat_spec_array[i] for i in test], -1)
    testY = np.array([sdr_array[i] for i in test])

    # Building convolutional network
    network = input_data(shape=[None, beat_spec_max, 1])
    network = conv_1d(network, 32, 4, activation='relu', regularizer="L2")
    network = max_pool_1d(network, 2)
    network = conv_1d(network, 64, 80, activation='relu', regularizer="L2")
    network = max_pool_1d(network, 2)
    network = fully_connected(network, 128, activation='relu')
    network = dropout(network, 0.8)
    network = fully_connected(network, 256, activation='relu') # look for non-tanh things???
    network = dropout(network, 0.8)
    network = fully_connected(network, 1, activation='linear')
    regress = tflearn.regression(network, optimizer='sgd', loss='mean_square', learning_rate=0.01)

    start = time.time()
    # Training
    model = tflearn.DNN(regress, tensorboard_verbose=1)
    model.fit(trainX, trainY, n_epoch=2000,
              snapshot_step=1000, show_metric=True, run_id='mir1k_2000_truncate')
    elapsed = (time.time() - start)

    predicted = np.array(model.predict(testX))[:,0]
    print("Test MSE: ", np.square(testY - predicted).mean())
    print(elapsed, "seconds")
    plot(testY, predicted)
Пример #16
0
    def generate_network(self):
        """ Return tflearn cnn network.
        """
        print(self.image_size, self.n_epoch, self.batch_size, self.person_ids)
        print(type(self.image_size), type(self.n_epoch),
              type(self.batch_size), type(self.person_ids))
        if not isinstance(self.image_size, list) \
            or not isinstance(self.n_epoch, int) \
            or not isinstance(self.batch_size, int) \
            or not isinstance(self.person_ids, list):
        # if self.image_size is None or self.n_epoch is None or \
        #     self.batch_size is None or self.person_ids is None:
            raise ValueError("Insufficient values to generate network.\n"
                             "Need (n_epoch, int), (batch_size, int),"
                             "(image_size, list), (person_ids, list).")

        # Real-time data preprocessing
        img_prep = ImagePreprocessing()
        img_prep.add_featurewise_zero_center()
        img_prep.add_featurewise_stdnorm()

        # Real-time data augmentation
        img_aug = ImageAugmentation()
        img_aug.add_random_rotation(max_angle=25.)
        img_aug.add_random_flip_leftright()

        # Convolutional network building
        network = input_data(
            shape=[None, self.image_size[0], self.image_size[1], 3],
            data_preprocessing=img_prep,
            data_augmentation=img_aug)
        network = conv_2d(network, self.image_size[0], self.IMAGE_CHANNEL_NUM,
                          activation='relu')
        network = max_pool_2d(network, 2)
        network = conv_2d(network, self.image_size[0] * 2,
                          self.IMAGE_CHANNEL_NUM,
                          activation='relu')
        network = conv_2d(network, self.image_size[0] * 2,
                          self.IMAGE_CHANNEL_NUM,
                          activation='relu')
        network = max_pool_2d(network, 2)
        network = fully_connected(network, self.image_size[0] * 2**4,
                                  activation='relu')
        network = dropout(network, 0.5)
        network = fully_connected(network, self.person_num,
                                  activation='softmax')
        network = regression(network, optimizer='adam',
                             loss='categorical_crossentropy',
                             learning_rate=0.001)
        return network
Пример #17
0
 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
Пример #18
0
def _model3():
    global yTest, img_aug
    tf.reset_default_graph()
    img_prep = ImagePreprocessing()
    img_prep.add_featurewise_zero_center()
    img_prep.add_featurewise_stdnorm()
    network = input_data(shape=[None, inputSize, inputSize, dim],
                             data_preprocessing=img_prep,
                             data_augmentation=img_aug)
    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, len(yTest[0]), activation='softmax')
    network = regression(network, optimizer='momentum',
                         loss='categorical_crossentropy',
                         learning_rate=0.001)
    print('Model has been made!!!?')
    # Training
    model = tflearn.DNN(network, checkpoint_path='model_densenet_cifar10',
                        max_checkpoints=10, tensorboard_verbose=0,
                        clip_gradients=0.)
    model.load(_path)
    pred = model.predict(xTest)

    df = pd.DataFrame(pred)
    df.to_csv(_path + ".csv")

    newList = pred.copy()
    newList = convert2(newList)
    if _CSV: makeCSV(newList)
    pred = convert2(pred)
    pred = convert3(pred)
    yTest = convert3(yTest)
    print(metrics.confusion_matrix(yTest, pred))
    print(metrics.classification_report(yTest, pred))
    print('Accuracy', accuracy_score(yTest, pred))
    print()
    if _wrFile: writeTest(pred)
Пример #19
0
def create_single_digit_model():
    input_layer, last_cnn_layer = create_cnn_layers()

    # h = Dense(256, activation='relu')(last_cnn_layer)
    h = fully_connected(last_cnn_layer, 256, activation='relu', weights_init=INIT)
    # h = Dropout(0.5)(h)
    h = dropout(h, 1-0.5)
    # output_layer = Dense(CLASS_COUNT, activation='softmax', name='out')(h)
    output_layer = fully_connected(h, CLASS_COUNT, activation='softmax', weights_init=INIT)
    network = regression(output_layer, optimizer=OPTIMIZER,
                     learning_rate=0.1,
                     loss='categorical_crossentropy', name='out')
    # model = Model(input_layer, output_layer)
    model = tflearn.DNN(network, tensorboard_verbose=3, tensorboard_dir='./logs/')
    return model
Пример #20
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def setup_model(checkpoint_path=None):
    """Sets up a deep belief network for image classification based on the set up described in

    :param checkpoint_path: string path describing prefix for model checkpoints
    :returns: Deep Neural Network
    :rtype: tflearn.DNN

    References:
        - Machine Learning is Fun! Part 3: Deep Learning and Convolutional Neural Networks

    Links:
        - https://medium.com/@ageitgey/machine-learning-is-fun-part-3-deep-learning-and-convolutional-neural-networks-f40359318721

    """
     # Make sure the data is normalized
    img_prep = ImagePreprocessing()
    img_prep.add_featurewise_zero_center()
    img_prep.add_featurewise_stdnorm()

    # Create extra synthetic training data by flipping, rotating and blurring the
    # images on our data set.
    img_aug = ImageAugmentation()
    img_aug.add_random_flip_leftright()
    img_aug.add_random_rotation(max_angle=25.)
    img_aug.add_random_blur(sigma_max=3.)

    # Input is a 32x32 image with 3 color channels (red, green and blue)
    network = input_data(shape=[None, 32, 32, 3],
                         data_preprocessing=img_prep,
                         data_augmentation=img_aug)
    network = conv_2d(network, 32, 3, activation='relu')
    network = max_pool_2d(network, 2)
    network = conv_2d(network, 64, 3, activation='relu')
    network = conv_2d(network, 64, 3, activation='relu')
    network = max_pool_2d(network, 2)
    network = fully_connected(network, 512, activation='relu')
    network = dropout(network, 0.5)
    network = fully_connected(network, 2, activation='softmax')
    network = regression(network, optimizer='adam',
                         loss='categorical_crossentropy',
                         learning_rate=0.001)
    if checkpoint_path:
        model = tflearn.DNN(network, tensorboard_verbose=3,
                            checkpoint_path=checkpoint_path)
    else:
        model = tflearn.DNN(network, tensorboard_verbose=3)

    return model
def main():
    """

    :return:
    """
    pickle_folder = '../NMF/mfcc_pickles'
    pickle_folders_to_load = [f for f in os.listdir(pickle_folder) if os.path.isdir(join(pickle_folder, f))]

    fg_or_bg = 'background'
    sdr_type = 'sdr'
    feature = 'mfcc_clusters'
    beat_spec_len = 432
    n_epochs = 200
    take = 1

    # set up training, testing, & validation partitions
    mfcc_array, sdr_array = load_mfcc_and_sdrs(pickle_folders_to_load, pickle_folder,
                                                    feature, fg_or_bg, sdr_type)

    mfcc_array = np.expand_dims(mfcc_array, -1)
    sdr_array = np.expand_dims(sdr_array, -1)

    # Building convolutional network
    network = input_data(shape=[None, 13, 100, 1])
    network = conv_2d(network, 32, [5, 5], activation='relu', regularizer="L2")
    network = max_pool_2d(network, 2)
    network = conv_2d(network, 64, [5, 5], activation='relu', regularizer="L2")
    network = max_pool_2d(network, 2)
    network = fully_connected(network, 128, activation='relu')
    network = dropout(network, 0.8)
    network = fully_connected(network, 256, activation='relu')
    network = dropout(network, 0.8)
    network = fully_connected(network, 1, activation='linear')
    regress = tflearn.regression(network, optimizer='rmsprop', loss='mean_square', learning_rate=0.001)

    start = time.time()
    # Training
    model = tflearn.DNN(regress)  # , session=sess)
    model.fit(mfcc_array, sdr_array, n_epoch=n_epochs,
              snapshot_step=1000, show_metric=True,
              run_id='repet_save_{0}_epochs_take_{1}'.format(n_epochs, take))
    elapsed = (time.time() - start)
    print('Finished training after ' + elapsed + 'seconds. Saving...')

    model_output_folder = 'network_outputs/'
    model_output_file = join(model_output_folder, 'nmf_save_{0}_epochs_take_{1}'.format(n_epochs, take))

    model.save(model_output_file)
Пример #22
0
def  do_cnn(trainX, trainY,testX, testY):
    global n_words
    # Data preprocessing
    # Sequence padding
    trainX = pad_sequences(trainX, maxlen=MAX_DOCUMENT_LENGTH, value=0.)
    testX = pad_sequences(testX, maxlen=MAX_DOCUMENT_LENGTH, value=0.)
    # Converting labels to binary vectors
    trainY = to_categorical(trainY, nb_classes=2)
    testY = to_categorical(testY, nb_classes=2)

    # Building convolutional network
    network = input_data(shape=[None, MAX_DOCUMENT_LENGTH], name='input')
    network = tflearn.embedding(network, input_dim=n_words+1, output_dim=128)
    branch1 = conv_1d(network, 128, 3, padding='valid', activation='relu', regularizer="L2")
    branch2 = conv_1d(network, 128, 4, padding='valid', activation='relu', regularizer="L2")
    branch3 = conv_1d(network, 128, 5, padding='valid', activation='relu', regularizer="L2")
    network = merge([branch1, branch2, branch3], mode='concat', axis=1)
    network = tf.expand_dims(network, 2)
    network = global_max_pool(network)
    network = dropout(network, 0.5)
    network = fully_connected(network, 2, activation='softmax')
    network = regression(network, optimizer='adam', learning_rate=0.001,
                         loss='categorical_crossentropy', name='target')
    # Training
    model = tflearn.DNN(network, tensorboard_verbose=0)
    model.fit(trainX, trainY, n_epoch = 20, shuffle=True, validation_set=(testX, testY), show_metric=True, batch_size=32)
Пример #23
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def inference(images, dataset_meta):
    """
    Build a tiny CNN model.

    Parameters
    ----------
    images : tensor
        Images returned from distorted_inputs() or inputs().
    dataset_meta : dict
        Has key 'n_classes'

    Returns
    -------
    logits
    """
    net = tf.reshape(images, [-1,
                              dataset_meta['image_width'],
                              dataset_meta['image_height'],
                              dataset_meta['image_depth']])
    net = tflearn.layers.conv.conv_2d(net,
                                      nb_filter=16,
                                      filter_size=3,
                                      activation='relu',
                                      strides=1,
                                      weight_decay=0.0)
    y_conv = fully_connected(net, dataset_meta['n_classes'],
                             activation='softmax',
                             weights_init='truncated_normal',
                             bias_init='zeros',
                             regularizer=None,
                             weight_decay=0)
    return y_conv
Пример #24
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def do_cnn_doc2vec(trainX, testX, trainY, testY):
    global max_features
    print "CNN and doc2vec"

    #trainX = pad_sequences(trainX, maxlen=max_features, value=0.)
    #testX = pad_sequences(testX, maxlen=max_features, value=0.)
    # Converting labels to binary vectors
    trainY = to_categorical(trainY, nb_classes=2)
    testY = to_categorical(testY, nb_classes=2)

    # Building convolutional network
    network = input_data(shape=[None,max_features], name='input')
    network = tflearn.embedding(network, input_dim=1000000, output_dim=128,validate_indices=False)
    branch1 = conv_1d(network, 128, 3, padding='valid', activation='relu', regularizer="L2")
    branch2 = conv_1d(network, 128, 4, padding='valid', activation='relu', regularizer="L2")
    branch3 = conv_1d(network, 128, 5, padding='valid', activation='relu', regularizer="L2")
    network = merge([branch1, branch2, branch3], mode='concat', axis=1)
    network = tf.expand_dims(network, 2)
    network = global_max_pool(network)
    network = dropout(network, 0.8)
    network = fully_connected(network, 2, activation='softmax')
    network = regression(network, optimizer='adam', learning_rate=0.001,
                         loss='categorical_crossentropy', name='target')
    # Training
    model = tflearn.DNN(network, tensorboard_verbose=0)
    model.fit(trainX, trainY,
              n_epoch=5, shuffle=True, validation_set=(testX, testY),
              show_metric=True, batch_size=100,run_id="review")
Пример #25
0
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
Пример #26
0
def inference(images, dataset_meta):
    """
    Build the CIFAR-10 model.

    Parameters
    ----------
    images : tensor
        Images returned from distorted_inputs() or inputs().
    dataset_meta : dict
        Has key 'n_classes'

    Returns
    -------
    logits
    """
    net = tf.reshape(images, [-1,
                              dataset_meta['image_width'],
                              dataset_meta['image_height'],
                              dataset_meta['image_depth']])
    net = tflearn.layers.core.flatten(net, name='Flatten')
    y_conv = fully_connected(net, dataset_meta['n_classes'],
                             activation='softmax',
                             weights_init='truncated_normal',
                             bias_init='zeros',
                             regularizer=None,
                             weight_decay=0)
    return y_conv
Пример #27
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def convolutional_neural_network(width=5, height=6):
    """Create the neural network model.

    Args:
        width: Width of the pseudo image
        height: Height of the pseudo image

    Returns:
        convnet: Output

    """
    # Initialize key variables
    conv1_filter_count = 32
    conv2_filter_count = 64
    fc_units = 1024
    image_height = height
    image_width = width
    filter_size = 2
    pooling_kernel_size = 2
    keep_probability = 0.6
    fully_connected_units = 10

    # Create the convolutional network stuff
    convnet = input_data(
        shape=[None, image_width, image_height, 1], name='input')

    convnet = conv_2d(
        convnet, conv1_filter_count, filter_size, activation='relu')
    convnet = max_pool_2d(convnet, pooling_kernel_size)

    convnet = conv_2d(
        convnet, conv2_filter_count, filter_size, activation='relu')
    convnet = max_pool_2d(convnet, pooling_kernel_size)

    convnet = fully_connected(convnet, fc_units, activation='relu')
    convnet = dropout(convnet, keep_probability)

    convnet = fully_connected(
        convnet, fully_connected_units, activation='softmax')
    convnet = regression(
        convnet,
        optimizer='adam',
        learning_rate=0.01,
        loss='categorical_crossentropy',
        name='targets')

    return convnet
Пример #28
0
def get_cnn_model(checkpoint_path='cnn_servo_model', width=72, height=48, depth=3, session=None):
    
    # Inputs
    network = input_data(shape=[None, height, width, depth], name='input')

    # Convolution no.1
    # Relu introduces non linearity into training
    network = conv_2d(network, 8, [5, 3], activation='relu')

    # Convolution no.2
    network = conv_2d(network, 12, [5, 8], activation='relu')
    
    # Convolution no.3
    network = conv_2d(network, 16, [5, 16], activation='relu')

    # Convolution no.4
    network = conv_2d(network, 24, [3, 20], activation='relu')

    # Convolution no.5
    network = conv_2d(network, 24, [3, 24], activation='relu')

    # Fully connected no.1
    network = fully_connected(network, 256, activation='relu')
    network = dropout(network, 0.8)

    # Fully connected no.2
    network = fully_connected(network, 100, activation='relu')
    network = dropout(network, 0.8)

    # Fully connected no.3
    network = fully_connected(network, 50, activation='relu')
    network = dropout(network, 0.8)

    # Fully connected no.4
    network = fully_connected(network, 10, activation='relu')
    network = dropout(network, 0.8)
 
    # Fully connected no.5
    network = fully_connected(network, 1, activation='tanh')

    # Regression
    network = regression(network, loss='mean_square', metric='accuracy', learning_rate=1e-4,name='target') 

    # Verbosity yay nay
    # 0 = nothing
    model = tflearn.DNN(network, tensorboard_verbose=2, checkpoint_path=checkpoint_path, session=session) 
    return model
Пример #29
0
def do_cnn_word2vec_2d(trainX, testX, trainY, testY):
    global max_features
    global max_document_length
    print "CNN and word2vec2d"
    y_test = testY
    #trainX = pad_sequences(trainX, maxlen=max_features, value=0.)
    #testX = pad_sequences(testX, maxlen=max_features, value=0.)
    # Converting labels to binary vectors
    trainY = to_categorical(trainY, nb_classes=2)
    testY = to_categorical(testY, nb_classes=2)

    # Building convolutional network
    network = input_data(shape=[None,max_document_length,max_features,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, 2, activation='softmax')
    network = regression(network, optimizer='adam', learning_rate=0.01,
                         loss='categorical_crossentropy', name='target')

    model = tflearn.DNN(network, tensorboard_verbose=0)
    model.fit(trainX, trainY,
              n_epoch=5, shuffle=True, validation_set=(testX, testY),
              show_metric=True,run_id="sms")

    y_predict_list = model.predict(testX)
    print y_predict_list

    y_predict = []
    for i in y_predict_list:
        print  i[0]
        if i[0] > 0.5:
            y_predict.append(0)
        else:
            y_predict.append(1)

    print(classification_report(y_test, y_predict))
    print metrics.confusion_matrix(y_test, y_predict)
Пример #30
0
def main():
    pickle_folder = '../pickles_rolloff'
    pickle_folders_to_load = [f for f in os.listdir(pickle_folder) if os.path.isdir(join(pickle_folder, f))]
    pickle_folders_to_load = sorted(pickle_folders_to_load)

    # pickle parameters
    fg_or_bg = 'background'
    sdr_type = 'sdr'
    feature = 'beat_spec'
    beat_spec_len = 432

    # training params
    n_classes = 16
    training_percent = 0.85
    testing_percent = 0.15
    validation_percent = 0.00


    # set up training, testing, & validation partitions
    beat_spec_array, sdr_array = load_beat_spec_and_sdrs(pickle_folders_to_load, pickle_folder,
                                                         feature, fg_or_bg, sdr_type)

    train, test, validate = split_into_sets(len(pickle_folders_to_load), training_percent,
                                            testing_percent, validation_percent)

    trainX = np.expand_dims([beat_spec_array[i] for i in train], -1)
    trainY = np.expand_dims([sdr_array[i] for i in train], -1)
    testX = np.expand_dims([beat_spec_array[i] for i in test], -1)
    testY = np.array([sdr_array[i] for i in test])

    # Building convolutional network
    input = input_data(shape=[None, beat_spec_len, 1])
    conv1 = conv_1d(input, 32, 10, activation='relu', regularizer="L2")
    max_pool1 = max_pool_1d(conv1, 2)
    full = fully_connected(max_pool1, 512, activation='tanh')
    # single = tflearn.single_unit(full)
    single = fully_connected(full, 1, activation='linear')
    regress = tflearn.regression(single, optimizer='sgd', loss='mean_square', learning_rate=0.01)

    # Training
    model = tflearn.DNN(regress, tensorboard_verbose=1)
    model.fit(trainX, trainY, n_epoch=500,
              snapshot_step=1000, show_metric=True, run_id='{} classes'.format(n_classes - 1))

    predicted = np.array(model.predict(testX))[:,0]
    plot(testY, predicted)
Пример #31
0
data_dir = "datasets/MNIST"
X, Y, X_test, Y_test = mnist.load_data(data_dir=data_dir, one_hot=True)
X = X.reshape([-1, 28, 28, 1])
X_test = X_test.reshape([-1, 28, 28, 1])

# Building the network
CNN = input_data(shape=[None, 28, 28, 1], name='input')
CNN = conv_2d(CNN, 32, 5, activation='relu', regularizer='L2')
CNN = max_pool_2d(CNN, 2)
CNN = local_response_normalization(CNN)

CNN = conv_2d(CNN, 64, 5, activation='relu', regularizer='L2')
CNN = max_pool_2d(CNN, 2)
CNN = local_response_normalization(CNN)

CNN = fully_connected(CNN, 1024, activation=None)
CNN = dropout(CNN, 0.5)
CNN = fully_connected(CNN, 10, activation='softmax')


CNN = regression(CNN, optimizer='adam', learning_rate=0.0001,
                 loss='categorical_crossentropy', name='target')

# Training the network
# the log dir will be created automaticlly
model = tflearn.DNN(CNN, tensorboard_verbose=0,
                   tensorboard_dir='logs/MNIST_tflearn_board/',
                   checkpoint_path='logs/MNIST_tflearn_checkpoints/checkpoint')

model.fit({'input': X}, {'target': Y}, n_epoch=3,
         validation_set=({'input': X_test}, {'target': Y_test}),
Пример #32
0
                net = bidirectional_rnn(net, BasicLSTMCell(cell_size[i]),
                                        BasicLSTMCell(cell_size[i]))
                net = dropout(net, dropout_ratio)
    elif cell_type == "gru":
        for i in range(len(cell_size)):
            if i < len(cell_size) - 1:
                net = bidirectional_rnn(net,
                                        GRUCell(cell_size[i]),
                                        GRUCell(cell_size[i]),
                                        return_seq=True)
                net = dropout(net, dropout_ratio)
            else:
                net = bidirectional_rnn(net, GRUCell(cell_size[i]),
                                        GRUCell(cell_size[i]))
                net = dropout(net, dropout_ratio)
    net = fully_connected(net, len(qualities), activation='softmax')
    net = regression(net,
                     optimizer='adam',
                     learning_rate=learning_rate,
                     loss='categorical_crossentropy')

    print('Train model')

    model = tflearn.DNN(net,
                        tensorboard_verbose=1,
                        tensorboard_dir="logdir/bi_lstm")

    print('Predict')
    model.fit(X_train,
              Y_train,
              validation_set=(X_test, Y_test),
Пример #33
0
    convnet = input_data(shape=[None, 128, 128, 1], name='input')

    convnet = conv_2d(convnet, 32, 3, activation='relu')
    convnet = max_pool_2d(convnet, kernel_size=2, strides=2)

    convnet = conv_2d(convnet, 64, 3, activation='relu')
    convnet = max_pool_2d(convnet, kernel_size=2, strides=2)

    convnet = conv_2d(convnet, 128, 3, activation='relu')
    convnet = max_pool_2d(convnet, kernel_size=2, strides=2)

    convnet = conv_2d(convnet, 256, 3, activation='relu')
    convnet = max_pool_2d(convnet, kernel_size=2, strides=2)

    convnet = fully_connected(convnet, 4096, activation='relu')
    convnet = dropout(convnet, 0.8)

    convnet = fully_connected(convnet, 2048, activation='relu')
    convnet = dropout(convnet, 0.8)

    convnet = fully_connected(convnet, 1024, activation='relu')
    convnet = dropout(convnet, 0.8)

    convnet = fully_connected(convnet, 8, activation='softmax')
    convnet = regression(convnet,
                         optimizer='adam',
                         learning_rate=0.00001,
                         loss='categorical_crossentropy',
                         name='targets')
Пример #34
0
    net, kernel_size=[2, 2])  # strides default to be the same as kernel_size

# layer2
net = conv_2d(net,
              nb_filter=64,
              filter_size=[5, 5],
              activation="relu",
              padding='same')
net = max_pool_2d(net, kernel_size=[2, 2])

####################################################################
# No need to reshape
####################################################################

# layer3
net = fully_connected(net, n_units=500, activation="relu")

# layer4
net = fully_connected(net, n_units=10, activation="softmax")

# calc optimizer

net = regression(net,
                 optimizer='sgd',
                 loss='categorical_crossentropy',
                 learning_rate=0.01)

# create Deep NN

model = tflearn.DNN(net, tensorboard_verbose=0)
Пример #35
0
resultado_olhos = tk.DoubleVar()

tf.reset_default_graph()

convnet = input_data(shape=[None, IMG_SIZE, IMG_SIZE, 1], name='input')

convnet = conv_2d(convnet, 20, 20, activation='prelu')
convnet = max_pool_2d(convnet, 10)

convnet = conv_2d(convnet, 25, 10, activation='prelu')
convnet = max_pool_2d(convnet, 5)

convnet = conv_2d(convnet, 30, 5, activation='prelu')
convnet = max_pool_2d(convnet, 5)

convnet = fully_connected(convnet, nInd * 10, activation='prelu')
convnet = dropout(convnet, 0.8)

convnet = fully_connected(convnet, nInd, activation='softmax')
convnet = regression(convnet,
                     optimizer='adam',
                     learning_rate=LR,
                     loss='categorical_crossentropy',
                     batch_size=16,
                     name='targets')
model = tflearn.DNN(convnet, tensorboard_dir='log')

if os.path.exists('{}.meta'.format(NOME_MODELO)):
    model.load(NOME_MODELO)

tf.reset_default_graph()
Пример #36
0
                    activation='relu',
                    regularizer='L2')
    pool2 = max_pool_2d(conv2, params['pool_width'], params['pool_stride'])
    lrn2 = local_response_normalization(pool2)

    conv3 = conv_2d(lrn2,
                    128,
                    params['conv_filter'],
                    activation='relu',
                    regularizer='L2')
    pool3 = max_pool_2d(conv3, params['pool_width'], params['pool_stride'])
    lrn3 = local_response_normalization(pool3)

    flat = flatten(lrn3)

    fully1 = fully_connected(lrn3, 384, activation='relu')
    drop1 = dropout(fully1, 0.5)
    fully2 = fully_connected(drop1, 384 / 2, activation='relu')
    drop2 = dropout(fully2, 0.5)
    fully3 = fully_connected(drop2, 10, activation='softmax')
    network = regression(fully3,
                         optimizer='adam',
                         loss='categorical_crossentropy',
                         learning_rate=0.001,
                         name='Target')

    # Train using classifier
    model = tflearn.DNN(network,
                        tensorboard_verbose=0,
                        tensorboard_dir='../log/')
    model.fit(X,
convnet = conv_2d(convnet, 32, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)

convnet = conv_2d(convnet, 64, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)

convnet = conv_2d(convnet, 128, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)

convnet = conv_2d(convnet, 64, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)

convnet = conv_2d(convnet, 32, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)

convnet = fully_connected(convnet, 1024, activation='relu')
convnet = dropout(convnet, 0.8)

convnet = fully_connected(convnet, 2, activation='softmax')
convnet = regression(convnet, optimizer='adam', learning_rate=LR, loss='categorical_crossentropy', name='targets')

model = tflearn.DNN(convnet, tensorboard_dir='log')

if os.path.exists('{}.meta'.format(MODEL_NAME)):
    model.load(MODEL_NAME)
    print('model loaded!')

train = train_data[:-500]
test = train_data[-500:]

X = np.array([i[0] for i in train]).reshape(-1, IMG_SIZE, IMG_SIZE, 1)
Пример #38
0
    def define_network(self):
        """
        Defines CNN architecture
        :return: CNN model
        """

        # My CNN 1 (type1)

        # # For data normalization
        # img_prep = ImagePreprocessing()
        # img_prep.add_featurewise_zero_center()
        # img_prep.add_featurewise_stdnorm()
        #
        # # For creating extra data(increase dataset). Flipped, Rotated, Blurred and etc. images
        # img_aug = ImageAugmentation()
        # img_aug.add_random_flip_leftright()
        # img_aug.add_random_rotation(max_angle=25.0)
        # img_aug.add_random_blur(sigma_max=3.0)
        #
        # self.network = input_data(shape=[None, IMG_SIZE, IMG_SIZE, 1],
        #                           data_augmentation=img_aug,
        #                           data_preprocessing=img_prep)
        # 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(EMOTIONS), activation='softmax')
        # self.network = regression(self.network, optimizer='adam', loss='categorical_crossentropy')
        # self.model = tflearn.DNN(self.network, checkpoint_path=os.path.join(CHECKPOINTS_PATH + '/emotion_recognition'),
        #                          max_checkpoints=1, tensorboard_verbose=0)

        # My CNN 2 (type2)

        # For creating extra data(increase dataset). Flipped, Rotated, Blurred and etc. images
        img_aug = ImageAugmentation()
        img_aug.add_random_flip_leftright()
        img_aug.add_random_rotation(max_angle=25.0)
        img_aug.add_random_blur(sigma_max=3.0)

        self.network = input_data(shape=[None, IMG_SIZE, IMG_SIZE, 1],
                                  data_augmentation=img_aug)

        self.network = conv_2d(self.network, 64, 3, activation='relu')
        self.network = batch_normalization(self.network)
        self.network = conv_2d(self.network, 64, 3, activation='relu')
        self.network = batch_normalization(self.network)
        self.network = max_pool_2d(self.network, 2, strides=2)

        self.network = conv_2d(self.network, 128, 3, activation='relu')
        self.network = batch_normalization(self.network)
        self.network = conv_2d(self.network, 128, 3, activation='relu')
        self.network = batch_normalization(self.network)
        self.network = max_pool_2d(self.network, 2, strides=2)
        self.network = dropout(self.network, 0.2)

        self.network = conv_2d(self.network, 256, 3, activation='relu')
        self.network = batch_normalization(self.network)
        self.network = conv_2d(self.network, 256, 3, activation='relu')
        self.network = batch_normalization(self.network)
        self.network = max_pool_2d(self.network, 2, strides=2)
        self.network = dropout(self.network, 0.25)

        self.network = conv_2d(self.network, 512, 3, activation='relu')
        self.network = batch_normalization(self.network)
        self.network = conv_2d(self.network, 512, 3, activation='relu')
        self.network = batch_normalization(self.network)
        self.network = max_pool_2d(self.network, 2, strides=2)
        self.network = dropout(self.network, 0.25)

        self.network = fully_connected(self.network, 1024, activation='relu')
        self.network = batch_normalization(self.network)
        self.network = dropout(self.network, 0.45)

        self.network = fully_connected(self.network, 1024, activation='relu')
        self.network = batch_normalization(self.network)
        self.network = dropout(self.network, 0.45)

        self.network = fully_connected(self.network,
                                       len(EMOTIONS),
                                       activation='softmax')
        self.network = regression(self.network,
                                  optimizer='adam',
                                  loss='categorical_crossentropy')

        self.model = tflearn.DNN(
            self.network,
            checkpoint_path=os.path.join(CHECKPOINTS_PATH +
                                         '/emotion_recognition'),
            max_checkpoints=1,
            tensorboard_verbose=0)

        return self.model
Пример #39
0
        drug_gru_1_candidate_bias.append(v)
    elif "GRU_3/GRU_3/GRUCell/Gates/Linear/Matrix" in v.name:
        drug_gru_2_gate_matrix.append(v)
    elif "GRU_3/GRU_3/GRUCell/Candidate/Linear/Matrix" in v.name:
        drug_gru_2_candidate_matrix.append(v)
    elif "GRU_3/GRU_3/GRUCell/Gates/Linear/Bias" in v.name:
        drug_gru_2_gate_bias.append(v)
    elif "GRU_3/GRU_3/GRUCell/Candidate/Linear/Bias" in v.name:
        drug_gru_2_candidate_bias.append(v)
    elif "Embedding_1" in v.name:
        drug_embd_W.append(v)

merging = merge([prot_reshape_6, drug_reshape_6], mode='concat', axis=1)
fc_1 = fully_connected(merging,
                       600,
                       activation='leakyrelu',
                       weights_init="xavier",
                       name='fully1')
drop_2 = dropout(fc_1, 0.8)
fc_2 = fully_connected(drop_2,
                       300,
                       activation='leakyrelu',
                       weights_init="xavier",
                       name='fully2')
drop_3 = dropout(fc_2, 0.8)
linear = fully_connected(drop_3, 1, activation='linear', name='fully3')
reg = regression(linear,
                 optimizer='adam',
                 learning_rate=0.001,
                 loss='mean_square',
                 name='target')
                                strides=1,
                                name='inception_5b_pool')
inception_5b_pool_1_1 = conv_2d(inception_5b_pool,
                                128,
                                filter_size=1,
                                activation='relu',
                                name='inception_5b_pool_1_1')
inception_5b_output = merge([
    inception_5b_1_1, inception_5b_3_3, inception_5b_5_5, inception_5b_pool_1_1
],
                            axis=3,
                            mode='concat')

pool5_7_7 = avg_pool_2d(inception_5b_output, kernel_size=7, strides=1)
pool5_7_7 = dropout(pool5_7_7, 0.4)
loss = fully_connected(pool5_7_7, 3, activation='softmax')
network = regression(loss,
                     optimizer='momentum',
                     loss='categorical_crossentropy',
                     learning_rate=0.001)
model = tflearn.DNN(network,
                    checkpoint_path='model_googlenet',
                    max_checkpoints=1,
                    tensorboard_verbose=2)
model.fit(X,
          Y,
          n_epoch=1000,
          validation_set=.25,
          shuffle=True,
          show_metric=True,
          batch_size=64,
Пример #41
0
                                          nb_filter=32,
                                          filter_size=3,
                                          activation='relu',
                                          strides=1,
                                          weight_decay=0.0)
        net = tflearn.layers.conv.conv_2d(net,
                                          nb_filter=32,
                                          filter_size=3,
                                          activation='relu',
                                          strides=1,
                                          weight_decay=0.0)
        net = tflearn.layers.core.flatten(net, name='Flatten')
        net = fully_connected(net,
                              1024,
                              activation='tanh',
                              weights_init='truncated_normal',
                              bias_init='zeros',
                              regularizer=None,
                              weight_decay=0)
        y_conv = fully_connected(net,
                                 369,
                                 activation='softmax',
                                 weights_init='truncated_normal',
                                 bias_init='zeros',
                                 regularizer=None,
                                 weight_decay=0)

        total_parameters = 0
        for variable in tf.trainable_variables():
            # shape is an array of tf.Dimension
            shape = variable.get_shape()
Пример #42
0
def make_model(x, y):

    print("X :", x.shape)
    print("Y :", y.shape)

    # Building convolutional network
    network = input_data(shape=[None, x.shape[1], x.shape[2], 1], name='input')

    #1
    # network = conv_2d(network, 128, activation='sigmoid', regularizer="L2")
    network = fully_connected(network, 128, activation='sigmoid')
    network = dropout(network, 0.8)
    print(network)
    #2
    # network = conv_2d(network, 128, activation='sigmoid', regularizer="L2")
    network = fully_connected(network, 128, activation='sigmoid')
    network = dropout(network, 0.8)
    print(network)
    #3
    network = fully_connected(network, 128, activation='sigmoid')
    network = dropout(network, 0.8)
    print(network)
    #4
    network = fully_connected(network, 128, activation='sigmoid')
    network = dropout(network, 0.8)
    #5
    network = fully_connected(network, 128, activation='sigmoid')
    network = dropout(network, 0.8)
    #6
    network = fully_connected(network, 128, activation='sigmoid')
    network = dropout(network, 0.8)
    #7
    network = fully_connected(network, 128, activation='sigmoid')
    network = dropout(network, 0.8)
    #8
    network = fully_connected(network, 128, activation='sigmoid')
    network = dropout(network, 0.8)
    #9
    network = fully_connected(network, 128, activation='sigmoid')
    network = dropout(network, 0.8)
    #10
    network = fully_connected(network, 128, activation='sigmoid')
    network = dropout(network, 0.8)

    network = fully_connected(network, y.shape[1], activation='sigmoid')
    network = regression(network,
                         optimizer='adam',
                         learning_rate=0.01,
                         loss='categorical_crossentropy',
                         name='target')

    # Training
    model = tflearn.DNN(
        network,
        checkpoint_path="./model/intermediate_models/model.ckpt",
        max_checkpoints=1,
        tensorboard_verbose=0)
    model.fit(
        {'input': x},
        {'target': y},
        n_epoch=100,
        batch_size=10,
        show_metric=True,
        snapshot_epoch=True,
    )

    return model
train, test, _ = imdb.load_data(path='imdb.pkl',
                                n_words=10000,
                                valid_portion=0.1)
trainX, trainY = train
testX, testY = test

# Data preprocessing
# Sequence padding
trainX = pad_sequences(trainX, maxlen=200, value=0.)
testX = pad_sequences(testX, maxlen=200, value=0.)
# Converting labels to binary vectors
trainY = to_categorical(trainY, nb_classes=2)
testY = to_categorical(testY, nb_classes=2)

# Network building
net = input_data(shape=[None, 200])
net = embedding(net, input_dim=20000, output_dim=128)
net = bidirectional_rnn(net, BasicLSTMCell(128), BasicLSTMCell(128))
net = dropout(net, 0.5)
net = fully_connected(net, 2, activation='softmax')
net = regression(net, optimizer='adam', loss='categorical_crossentropy')

# Training
model = tflearn.DNN(net, clip_gradients=0., tensorboard_verbose=2)
model.fit(trainX,
          trainY,
          n_epoch=1,
          validation_set=0.1,
          show_metric=True,
          batch_size=64)
Пример #44
0
def foo(img_fn, model_fn='../data/model/model_weights'):
    img = cv2.imread(img_fn, cv2.IMREAD_GRAYSCALE)

    haar_fn = '../data/haarcascade_russian_plate_number.xml'
    haar = cv2.CascadeClassifier(haar_fn)
    detected = haar.detectMultiScale(img)
    plates = []
    for x, y, w, h in detected:
        obj = img[y:y + h, x:x + w]
        plates.append(obj)

    chars = plates[0] < filters.threshold_minimum(plates[0])

    labeled_chars, a = ndi.label(chars)
    labeled_chars = (labeled_chars > 1).astype(np.int8)

    c = measure.find_contours(labeled_chars, .1)

    letters = []
    for i, v in enumerate(c):
        xs, ys = zip(*[i for i in v])
        x = int(min(xs))
        y = int(min(ys))
        w = int(max(xs) - x + 2)
        h = int(max(ys) - y + 2)
        if w < 15:
            continue
        letters.append((y, x, h, w))

    letters = sorted(letters)

    letters_img = [plates[0][x:x + w, y:y + h] for y, x, h, w in letters]

    letters_img = [i for i in letters_img if i[0, 0] > 127]

    sizes = [image.size for image in letters_img]
    median = np.median(sizes)
    allowed_size = median + median / 4

    letters_img = [image for image in letters_img if image.size < allowed_size]

    size = 64

    normalized_img = []
    for i in letters_img:
        ratio = i.shape[0] / i.shape[1]
        img1 = transform.resize(i, [size, int(size / ratio)], mode='constant')
        width = img1.shape[1]
        missing = (size - width) // 2
        ones = np.ones([size, missing])
        img2 = np.append(ones, img1, 1)
        img3 = np.append(img2, ones, 1)
        if 2 * missing + width != size:
            one = np.ones([size, 1])
            img4 = np.append(img3, one, 1)
        else:
            img4 = img3
        normalized_img.append(img4 * 255)

    net_input = input_data(shape=[None, 64, 64, 1])

    conv1 = conv_2d(net_input,
                    nb_filter=4,
                    filter_size=5,
                    strides=[1, 1, 1, 1],
                    activation='relu')
    max_pool1 = max_pool_2d(conv1, kernel_size=2)

    conv2 = conv_2d(max_pool1,
                    nb_filter=8,
                    filter_size=5,
                    strides=[1, 2, 2, 1],
                    activation='relu')
    max_pool2 = max_pool_2d(conv2, kernel_size=2)

    conv3 = conv_2d(max_pool2,
                    nb_filter=12,
                    filter_size=4,
                    strides=[1, 1, 1, 1],
                    activation='relu')
    max_pool3 = max_pool_2d(conv3, kernel_size=2)

    fc1 = fully_connected(max_pool3, n_units=200, activation='relu')
    drop1 = dropout(fc1, keep_prob=.5)

    fc2 = fully_connected(drop1, n_units=36, activation='softmax')
    net = regression(fc2)

    model = DNN(network=net)
    model.load(model_file=model_fn)

    labels = list('ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789')

    predicted = []
    for i in normalized_img:
        y = model.predict(i.reshape([1, 64, 64, 1]))
        y_pred = np.argmax(y[0])
        predicted.append(labels[y_pred])

    return ''.join(predicted)
Пример #45
0
"""
Define Convolutional Nerual Network model for MNIST input
"""

from tflearn import DNN
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.normalization import local_response_normalization
from tflearn.layers.estimator import regression

# 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')

# Define model
model = DNN(network, tensorboard_verbose=0)
Пример #46
0
network = conv_2d(network,
                  64,
                  5,
                  strides=2,
                  activation='relu',
                  weights_init='truncated_normal',
                  bias_init='truncated_normal')
network = conv_2d(network,
                  64,
                  5,
                  strides=2,
                  activation='relu',
                  weights_init='truncated_normal',
                  bias_init='truncated_normal')
network = max_pool_2d(network, 2, strides=2)
network = fully_connected(network, 256, activation='relu')
network = dropout(network, dropout_rate)
network = fully_connected(network, n_classes, activation='softmax')
network = regression(network,
                     optimizer='adam',
                     loss='categorical_crossentropy',
                     learning_rate=learning_rate)

# Training
model = tflearn.DNN(network,
                    checkpoint_path='model.tfl.ckpt',
                    tensorboard_verbose=0,
                    max_checkpoints=1)

model.load("model.tfl")
Пример #47
0
# In[8]:

#first convolution Layer
model = conv_2d(model, 46, 5, activation='relu')
model = max_pool_2d(model, 5)

#second Convolution Layer
model = conv_2d(model, 42, 5, activation='relu')
model = max_pool_2d(model, 5)

#third Convolution Layer
model = conv_2d(model, 38, 5, activation='relu')
model = max_pool_2d(model, 5)

#fully Connected Layer
model = fully_connected(model, 1024, activation='relu')
model = dropout(model, 0.7)

model = fully_connected(model, 2, activation='softmax')

# In[9]:

model = regression(model,
                   optimizer='adam',
                   loss='categorical_crossentropy',
                   learning_rate=0.003,
                   name='output')

# In[10]:

cnnmodel = tflearn.DNN(model)
train_data = create_train_data()
convnet = input_data(shape=[None, IMG_SIZE, IMG_SIZE, 3], name='input')
convnet = conv_2d(
    convnet, 32, 5,
    activation='relu')  # Convolution Layer-1 with 5 Filters of size 32*32
convnet = max_pool_2d(convnet, 5)  # Max Pooling with filter size of 5*5
convnet = conv_2d(
    convnet, 64, 5,
    activation='relu')  # Convolution Layer-2 with 5 Filters of size 64*64
convnet = max_pool_2d(convnet, 5)  # Max pooling with filter size of 5*5
convnet = conv_2d(
    convnet, 32, 5,
    activation='relu')  # Convolution Layer-3 with 5 Filters of size 32*32
convnet = max_pool_2d(convnet, 5)  # Max pooling with filter size of 5*5
convnet = fully_connected(
    convnet, 1024,
    activation='relu')  # Fully Connected Layer-4 with 1024 neurons
convnet = dropout(convnet, 0.4)  # Dropout rate set to 0.4
convnet = fully_connected(convnet, 2, activation='softmax')
convnet = regression(convnet,
                     optimizer='adam',
                     learning_rate=LR,
                     loss='categorical_crossentropy',
                     name='targets')
model = tflearn.DNN(convnet, tensorboard_dir='log')

##############################################################################
# Modelling the training data
train = train_data
X = np.array([i[0] for i in train])
Y = [i[1] for i in train]
Пример #49
0
    x = x.reshape([-1, 28, 28, 1])
    test_x = test_x.reshape([-1, 28, 28, 1])

    input_data = input_data(shape=[None, 28, 28, 1], name='input')

    # nb_filter表示输出通道数,filter_size表示卷积核大小5x5
    conv1 = conv_2d(input_data, nb_filter=32, filter_size=5, activation='relu')

    # kernel_size表示池化窗口大小2x2
    pool1 = max_pool_2d(conv1, kernel_size=2)

    conv2 = conv_2d(pool1, nb_filter=64, filter_size=5, activation='relu')

    pool2 = max_pool_2d(conv2, kernel_size=2)

    fc = fully_connected(pool2, n_units=1024, activation='relu')
    fc = dropout(fc, 0.8)

    output = fully_connected(fc, n_units=10, activation='softmax')

    network = regression(output,
                         optimizer='adam',
                         learning_rate=0.01,
                         loss='categorical_crossentropy',
                         name='targets')

    model = tflearn.DNN(network, tensorboard_dir='log')

    if os.path.exists('tflearncnn.model.index') and os.path.exists(
            'tflearncnn.model.meta'):
        model.load('tflearncnn.model')
Пример #50
0
import tflearn as tf
from tflearn.layers.core import input_data,dropout,fully_connected
from tflearn.layers.estimator import regression
from tflearn.data_utils import load_csv
import tensorflow as tfs


data,target=load_csv('dataset.csv',target_column=-1,columns_to_ignore=[1],has_header=True,categorical_labels=True,n_classes=2)

network = input_data(shape=[None,23],name='input')
network = fully_connected(network,10,activation='relu',name='nn_layer_1')
network = fully_connected(network,5,activation='relu',name='nn_layer_2')
network = fully_connected(network,2,activation='softmax',name='output_layer')
network = regression(network,optimizer='adam',loss='categorical_crossentropy',learning_rate=0.001)
model = tf.DNN(network, tensorboard_verbose=3)
model.fit(data,target,n_epoch=50,validation_set=0.3,show_metric=True,run_id='model1')
#model.save('/model1')
Пример #51
0
def analysis():
    import cv2
    import numpy as np
    import os
    from random import shuffle
    from tqdm import \
        tqdm
    verify_dir = 'testpicture'
    IMG_SIZE = 50
    LR = 1e-3
    MODEL_NAME = 'healthyvsunhealthy-{}-{}.model'.format(LR, '2conv-basic')

    def process_verify_data():
        verifying_data = []
        for img in tqdm(os.listdir(verify_dir)):
            path = os.path.join(verify_dir, img)
            img_num = img.split('.')[0]
            img = cv2.imread(path, cv2.IMREAD_COLOR)
            img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))
            verifying_data.append([np.array(img), img_num])
        np.save('verify_data.npy', verifying_data)
        return verifying_data

    verify_data = process_verify_data()

    import tflearn
    from tflearn.layers.conv import conv_2d, max_pool_2d
    from tflearn.layers.core import input_data, dropout, fully_connected
    from tflearn.layers.estimator import regression
    import tensorflow as tf
    tf.reset_default_graph()

    convnet = input_data(shape=[None, IMG_SIZE, IMG_SIZE, 3], name='input')

    convnet = conv_2d(convnet, 32, 3, activation='relu')
    convnet = max_pool_2d(convnet, 3)

    convnet = conv_2d(convnet, 64, 3, activation='relu')
    convnet = max_pool_2d(convnet, 3)

    convnet = conv_2d(convnet, 128, 3, activation='relu')
    convnet = max_pool_2d(convnet, 3)

    convnet = conv_2d(convnet, 32, 3, activation='relu')
    convnet = max_pool_2d(convnet, 3)

    convnet = conv_2d(convnet, 64, 3, activation='relu')
    convnet = max_pool_2d(convnet, 3)

    convnet = fully_connected(convnet, 1024, activation='relu')
    convnet = dropout(convnet, 0.8)

    convnet = fully_connected(convnet, 4, activation='softmax')
    convnet = regression(convnet,
                         optimizer='adam',
                         learning_rate=LR,
                         loss='categorical_crossentropy',
                         name='targets')

    model = tflearn.DNN(convnet, tensorboard_dir='log')

    if os.path.exists('{}.meta'.format(MODEL_NAME)):
        model.load(MODEL_NAME)
        print('model loaded!')

    import matplotlib.pyplot as plt

    fig = plt.figure()

    for num, data in enumerate(verify_data):

        img_num = data[1]
        img_data = data[0]

        y = fig.add_subplot(3, 4, num + 1)
        orig = img_data
        data = img_data.reshape(IMG_SIZE, IMG_SIZE, 3)
        # model_out = model.predict([data])[0]
        model_out = model.predict([data])[0]

        if np.argmax(model_out) == 0:
            str_label = 'healthy'
        elif np.argmax(model_out) == 1:
            str_label = 'bacterial'
        elif np.argmax(model_out) == 2:
            str_label = 'viral'
        elif np.argmax(model_out) == 3:
            str_label = 'lateblight'

        if str_label == 'healthy':
            status = "HEALTHY"
        else:
            status = "UNHEALTHY"

        message = tk.Label(text='Status: ' + status,
                           background="lightgreen",
                           fg="Brown",
                           font=("", 15))
        message.grid(column=0, row=3, padx=10, pady=10)
        if str_label == 'bacterial':
            diseasename = "Bacterial Spot "
            disease = tk.Label(text='Disease Name: ' + diseasename,
                               background="lightgreen",
                               fg="Black",
                               font=("", 15))
            disease.grid(column=0, row=4, padx=10, pady=10)
            r = tk.Label(text='Click below for remedies...',
                         background="lightgreen",
                         fg="Brown",
                         font=("", 15))
            r.grid(column=0, row=5, padx=10, pady=10)
            button3 = tk.Button(text="Remedies", command=bact)
            button3.grid(column=0, row=6, padx=10, pady=10)
        elif str_label == 'viral':
            diseasename = "Yellow leaf curl virus "
            disease = tk.Label(text='Disease Name: ' + diseasename,
                               background="lightgreen",
                               fg="Black",
                               font=("", 15))
            disease.grid(column=0, row=4, padx=10, pady=10)
            r = tk.Label(text='Click below for remedies...',
                         background="lightgreen",
                         fg="Brown",
                         font=("", 15))
            r.grid(column=0, row=5, padx=10, pady=10)
            button3 = tk.Button(text="Remedies", command=vir)
            button3.grid(column=0, row=6, padx=10, pady=10)
        elif str_label == 'lateblight':
            diseasename = "Late Blight "
            disease = tk.Label(text='Disease Name: ' + diseasename,
                               background="lightgreen",
                               fg="Black",
                               font=("", 15))
            disease.grid(column=0, row=4, padx=10, pady=10)
            r = tk.Label(text='Click below for remedies...',
                         background="lightgreen",
                         fg="Brown",
                         font=("", 15))
            r.grid(column=0, row=5, padx=10, pady=10)
            button3 = tk.Button(text="Remedies", command=latebl)
            button3.grid(column=0, row=6, padx=10, pady=10)
        else:
            r = tk.Label(text='Plant is healthy',
                         background="lightgreen",
                         fg="Black",
                         font=("", 15))
            r.grid(column=0, row=4, padx=10, pady=10)
            button = tk.Button(text="Exit", command=exit)
            button.grid(column=0, row=9, padx=20, pady=20)
Пример #52
0
def create_googlenet(num_classes):
    # Building 'GoogleNet'
    network = input_data(shape=[None, 227, 227, 3],
                         data_preprocessing=img_prep,
                         data_augmentation=img_aug)
    conv1_7_7 = conv_2d(network, 64, 7, strides=2, activation='relu', name='conv1_7_7_s2')
    pool1_3_3 = max_pool_2d(conv1_7_7, 3, strides=2)
    pool1_3_3 = local_response_normalization(pool1_3_3)
    conv2_3_3_reduce = conv_2d(pool1_3_3, 64, 1, activation='relu', name='conv2_3_3_reduce')
    conv2_3_3 = conv_2d(conv2_3_3_reduce, 192, 3, activation='relu', name='conv2_3_3')
    conv2_3_3 = local_response_normalization(conv2_3_3)
    pool2_3_3 = max_pool_2d(conv2_3_3, kernel_size=3, strides=2, name='pool2_3_3_s2')

    # 3a
    inception_3a_1_1 = conv_2d(pool2_3_3, 64, 1, activation='relu', name='inception_3a_1_1')
    inception_3a_3_3_reduce = conv_2d(pool2_3_3, 96, 1, activation='relu', name='inception_3a_3_3_reduce')
    inception_3a_3_3 = conv_2d(inception_3a_3_3_reduce, 128, filter_size=3,  activation='relu', name='inception_3a_3_3')
    inception_3a_5_5_reduce = conv_2d(pool2_3_3, 16, filter_size=1, activation='relu', name='inception_3a_5_5_reduce')
    inception_3a_5_5 = conv_2d(inception_3a_5_5_reduce, 32, filter_size=5, activation='relu', name='inception_3a_5_5')
    inception_3a_pool = max_pool_2d(pool2_3_3, kernel_size=3, strides=1, name='inception_3a_pool')
    inception_3a_pool_1_1 = conv_2d(inception_3a_pool, 32, filter_size=1, activation='relu', name='inception_3a_pool_1_1')
    inception_3a_output = merge([inception_3a_1_1, inception_3a_3_3, inception_3a_5_5, inception_3a_pool_1_1], mode='concat', axis=3)

    # 3b
    inception_3b_1_1 = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_1_1')
    inception_3b_3_3_reduce = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_3_3_reduce')
    inception_3b_3_3 = conv_2d(inception_3b_3_3_reduce, 192, filter_size=3, activation='relu', name='inception_3b_3_3')
    inception_3b_5_5_reduce = conv_2d(inception_3a_output, 32, filter_size=1, activation='relu', name='inception_3b_5_5_reduce')
    inception_3b_5_5 = conv_2d(inception_3b_5_5_reduce, 96, filter_size=5,  name='inception_3b_5_5')
    inception_3b_pool = max_pool_2d(inception_3a_output, kernel_size=3, strides=1,  name='inception_3b_pool')
    inception_3b_pool_1_1 = conv_2d(inception_3b_pool, 64, filter_size=1, activation='relu', name='inception_3b_pool_1_1')
    inception_3b_output = merge([inception_3b_1_1, inception_3b_3_3, inception_3b_5_5, inception_3b_pool_1_1], mode='concat', axis=3, name='inception_3b_output')
    pool3_3_3 = max_pool_2d(inception_3b_output, kernel_size=3, strides=2, name='pool3_3_3')

    # 4a
    inception_4a_1_1 = conv_2d(pool3_3_3, 192, filter_size=1, activation='relu', name='inception_4a_1_1')
    inception_4a_3_3_reduce = conv_2d(pool3_3_3, 96, filter_size=1, activation='relu', name='inception_4a_3_3_reduce')
    inception_4a_3_3 = conv_2d(inception_4a_3_3_reduce, 208, filter_size=3,  activation='relu', name='inception_4a_3_3')
    inception_4a_5_5_reduce = conv_2d(pool3_3_3, 16, filter_size=1, activation='relu', name='inception_4a_5_5_reduce')
    inception_4a_5_5 = conv_2d(inception_4a_5_5_reduce, 48, filter_size=5,  activation='relu', name='inception_4a_5_5')
    inception_4a_pool = max_pool_2d(pool3_3_3, kernel_size=3, strides=1,  name='inception_4a_pool')
    inception_4a_pool_1_1 = conv_2d(inception_4a_pool, 64, filter_size=1, activation='relu', name='inception_4a_pool_1_1')
    inception_4a_output = merge([inception_4a_1_1, inception_4a_3_3, inception_4a_5_5, inception_4a_pool_1_1], mode='concat', axis=3, name='inception_4a_output')

    # 4b
    inception_4b_1_1 = conv_2d(inception_4a_output, 160, filter_size=1, activation='relu', name='inception_4a_1_1')
    inception_4b_3_3_reduce = conv_2d(inception_4a_output, 112, filter_size=1, activation='relu', name='inception_4b_3_3_reduce')
    inception_4b_3_3 = conv_2d(inception_4b_3_3_reduce, 224, filter_size=3, activation='relu', name='inception_4b_3_3')
    inception_4b_5_5_reduce = conv_2d(inception_4a_output, 24, filter_size=1, activation='relu', name='inception_4b_5_5_reduce')
    inception_4b_5_5 = conv_2d(inception_4b_5_5_reduce, 64, filter_size=5,  activation='relu', name='inception_4b_5_5')
    inception_4b_pool = max_pool_2d(inception_4a_output, kernel_size=3, strides=1,  name='inception_4b_pool')
    inception_4b_pool_1_1 = conv_2d(inception_4b_pool, 64, filter_size=1, activation='relu', name='inception_4b_pool_1_1')
    inception_4b_output = merge([inception_4b_1_1, inception_4b_3_3, inception_4b_5_5, inception_4b_pool_1_1], mode='concat', axis=3, name='inception_4b_output')

    # 4c
    inception_4c_1_1 = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu', name='inception_4c_1_1')
    inception_4c_3_3_reduce = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu', name='inception_4c_3_3_reduce')
    inception_4c_3_3 = conv_2d(inception_4c_3_3_reduce, 256,  filter_size=3, activation='relu', name='inception_4c_3_3')
    inception_4c_5_5_reduce = conv_2d(inception_4b_output, 24, filter_size=1, activation='relu', name='inception_4c_5_5_reduce')
    inception_4c_5_5 = conv_2d(inception_4c_5_5_reduce, 64,  filter_size=5, activation='relu', name='inception_4c_5_5')
    inception_4c_pool = max_pool_2d(inception_4b_output, kernel_size=3, strides=1)
    inception_4c_pool_1_1 = conv_2d(inception_4c_pool, 64, filter_size=1, activation='relu', name='inception_4c_pool_1_1')
    inception_4c_output = merge([inception_4c_1_1, inception_4c_3_3, inception_4c_5_5, inception_4c_pool_1_1], mode='concat', axis=3, name='inception_4c_output')

    # 4d
    inception_4d_1_1 = conv_2d(inception_4c_output, 112, filter_size=1, activation='relu', name='inception_4d_1_1')
    inception_4d_3_3_reduce = conv_2d(inception_4c_output, 144, filter_size=1, activation='relu', name='inception_4d_3_3_reduce')
    inception_4d_3_3 = conv_2d(inception_4d_3_3_reduce, 288, filter_size=3, activation='relu', name='inception_4d_3_3')
    inception_4d_5_5_reduce = conv_2d(inception_4c_output, 32, filter_size=1, activation='relu', name='inception_4d_5_5_reduce')
    inception_4d_5_5 = conv_2d(inception_4d_5_5_reduce, 64, filter_size=5,  activation='relu', name='inception_4d_5_5')
    inception_4d_pool = max_pool_2d(inception_4c_output, kernel_size=3, strides=1,  name='inception_4d_pool')
    inception_4d_pool_1_1 = conv_2d(inception_4d_pool, 64, filter_size=1, activation='relu', name='inception_4d_pool_1_1')
    inception_4d_output = merge([inception_4d_1_1, inception_4d_3_3, inception_4d_5_5, inception_4d_pool_1_1], mode='concat', axis=3, name='inception_4d_output')

    # 4e
    inception_4e_1_1 = conv_2d(inception_4d_output, 256, filter_size=1, activation='relu', name='inception_4e_1_1')
    inception_4e_3_3_reduce = conv_2d(inception_4d_output, 160, filter_size=1, activation='relu', name='inception_4e_3_3_reduce')
    inception_4e_3_3 = conv_2d(inception_4e_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_4e_3_3')
    inception_4e_5_5_reduce = conv_2d(inception_4d_output, 32, filter_size=1, activation='relu', name='inception_4e_5_5_reduce')
    inception_4e_5_5 = conv_2d(inception_4e_5_5_reduce, 128,  filter_size=5, activation='relu', name='inception_4e_5_5')
    inception_4e_pool = max_pool_2d(inception_4d_output, kernel_size=3, strides=1,  name='inception_4e_pool')
    inception_4e_pool_1_1 = conv_2d(inception_4e_pool, 128, filter_size=1, activation='relu', name='inception_4e_pool_1_1')
    inception_4e_output = merge([inception_4e_1_1, inception_4e_3_3, inception_4e_5_5, inception_4e_pool_1_1], axis=3, mode='concat')
    pool4_3_3 = max_pool_2d(inception_4e_output, kernel_size=3, strides=2, name='pool_3_3')

    # 5a
    inception_5a_1_1 = conv_2d(pool4_3_3, 256, filter_size=1, activation='relu', name='inception_5a_1_1')
    inception_5a_3_3_reduce = conv_2d(pool4_3_3, 160, filter_size=1, activation='relu', name='inception_5a_3_3_reduce')
    inception_5a_3_3 = conv_2d(inception_5a_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_5a_3_3')
    inception_5a_5_5_reduce = conv_2d(pool4_3_3, 32, filter_size=1, activation='relu', name='inception_5a_5_5_reduce')
    inception_5a_5_5 = conv_2d(inception_5a_5_5_reduce, 128, filter_size=5,  activation='relu', name='inception_5a_5_5')
    inception_5a_pool = max_pool_2d(pool4_3_3, kernel_size=3, strides=1,  name='inception_5a_pool')
    inception_5a_pool_1_1 = conv_2d(inception_5a_pool, 128, filter_size=1, activation='relu', name='inception_5a_pool_1_1')
    inception_5a_output = merge([inception_5a_1_1, inception_5a_3_3, inception_5a_5_5, inception_5a_pool_1_1], axis=3, mode='concat')

    # 5b
    inception_5b_1_1 = conv_2d(inception_5a_output, 384, filter_size=1, activation='relu', name='inception_5b_1_1')
    inception_5b_3_3_reduce = conv_2d(inception_5a_output, 192, filter_size=1, activation='relu', name='inception_5b_3_3_reduce')
    inception_5b_3_3 = conv_2d(inception_5b_3_3_reduce, 384,  filter_size=3, activation='relu', name='inception_5b_3_3')
    inception_5b_5_5_reduce = conv_2d(inception_5a_output, 48, filter_size=1, activation='relu', name='inception_5b_5_5_reduce')
    inception_5b_5_5 = conv_2d(inception_5b_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_5b_5_5')
    inception_5b_pool = max_pool_2d(inception_5a_output, kernel_size=3, strides=1,  name='inception_5b_pool')
    inception_5b_pool_1_1 = conv_2d(inception_5b_pool, 128, filter_size=1, activation='relu', name='inception_5b_pool_1_1')
    inception_5b_output = merge([inception_5b_1_1, inception_5b_3_3, inception_5b_5_5, inception_5b_pool_1_1], axis=3, mode='concat')
    pool5_7_7 = avg_pool_2d(inception_5b_output, kernel_size=7, strides=1)
    pool5_7_7 = dropout(pool5_7_7, 0.4)

    #fc
    loss = fully_connected(pool5_7_7, num_classes, activation='softmax')
    network = regression(loss, optimizer='momentum',
                     loss='categorical_crossentropy',
                     learning_rate=0.01)

    return network
Пример #53
0
def analysis(filepath):

	verify_data = process_verify_data(filepath)

	str_label = "Cannot make a prediction."
	status = "Error"

	tf.reset_default_graph()

	convnet = input_data(shape=[None, IMG_SIZE, IMG_SIZE, 3], name='input')

	'''
	# relu:

	Relu is used in the middle / hidden layers of the network to regularize the activation.
	It is essentialy the function: max(0, x)
	Activation should not be in negative, either it should be zero or more than that.

	# softmax: 

	Softmax is used for the output layer in multi class classification problems.
	It is essentialy the function: log(1 + e^x)
	It outputs a vector of probabilities of each class.

	'''

	convnet = conv_2d(convnet, 32, 3, activation='relu')
	convnet = max_pool_2d(convnet, 3)

	convnet = conv_2d(convnet, 64, 3, activation='relu')
	convnet = max_pool_2d(convnet, 3)

	convnet = conv_2d(convnet, 128, 3, activation='relu')
	convnet = max_pool_2d(convnet, 3)

	convnet = conv_2d(convnet, 32, 3, activation='relu')
	convnet = max_pool_2d(convnet, 3)

	convnet = conv_2d(convnet, 64, 3, activation='relu')
	convnet = max_pool_2d(convnet, 3)

	convnet = fully_connected(convnet, 1024, activation='relu')
	convnet = dropout(convnet, 0.8)

	convnet = fully_connected(convnet, 4, activation='softmax')
	convnet = regression(convnet, optimizer='adam', learning_rate=LR, loss='categorical_crossentropy', name='targets')

	model = tflearn.DNN(convnet, tensorboard_dir='log')

	if os.path.exists('{}.meta'.format(MODEL_NAME)):
		model.load(MODEL_NAME)
		print ('Model loaded successfully.')
	else:
		print ('Error: Create a model using neural_network.py first.')

	img_data, img_name = verify_data[0], verify_data[1]

	orig = img_data
	data = img_data.reshape(IMG_SIZE, IMG_SIZE, 3)

	model_out = model.predict([data])[0]

	if np.argmax(model_out) == 0: str_label = 'Healthy'
	elif np.argmax(model_out) == 1: str_label = 'Bacterial'
	elif np.argmax(model_out) == 2: str_label = 'Viral'
	elif np.argmax(model_out) == 3: str_label = 'Lateblight'

	if str_label =='Healthy': status = 'Healthy'
	else: status = 'Unhealthy'

	result = 'Status: ' + status + '.'
	
	if (str_label != 'Healthy'): result += '\nDisease: ' + str_label + '.'

	return result
Пример #54
0
print(type(feature_vector_train), feature_vector_train.shape)
print(type(labels_train), labels_train.shape)

num_features = 128  # output of the resnet
classes = num_faces  # number of faces to be recognized
n1 = 68  # number of nodes in hidden layer 1
n2 = 68  # number of nodes in hidden layer 2
n3 = 32  # number of nodes in hidden layer 3
n4 = 16  # number of nodes in hidden layer 3
n5 = 16  # number of nodes in hidden layer 3
learning_rate = 0.03
num_epochs = 100

output = input_data(shape=[None, num_features, 1, 1], name="input")

output = fully_connected(output, n1, activation="relu")

# output = fully_connected(output, n2, activation="relu")
#
# output = fully_connected(output, n3, activation="relu")
#
# output = fully_connected(output, n4, activation="relu")
#
# output = fully_connected(output, n5, activation="relu")

output = fully_connected(output, classes, activation="softmax")

output = regression(output,
                    optimizer="adam",
                    learning_rate=learning_rate,
                    loss="categorical_crossentropy",
Пример #55
0
def test1(x_train, y_train, x_test, y_test):
    # Train using classifier

    #define network
    int_put = input_data(shape=[None, 224, 5, 5, 1], )

    conv1 = conv_3d(int_put,
                    24, [24, 3, 3],
                    padding='VALID',
                    strides=[1, 1, 1, 1, 1],
                    activation='prelu',
                    weight_decay=0.05)
    print('conv1', conv1.get_shape().as_list())
    batch_norm = batch_normalization(conv1)
    #act1=tflearn.activations.relu(batch_norm)
    #pool1=max_pool_3d(act1,[1,1,2,2,1],strides=[1,1,1,1,1])

    conv2 = conv_3d(batch_norm,
                    12, [24, 3, 3],
                    padding='VALID',
                    strides=[1, 1, 1, 1, 1],
                    activation='prelu',
                    weight_decay=0.05)
    print('conv2', conv2.get_shape().as_list())
    batch_norm = batch_normalization(conv2)
    #act = tflearn.activations.relu(batch_norm)
    #pool2=max_pool_3d(act,[1,1,2,2,1],strides=[1,1,1,1,1])

    net = residual_block_concat(batch_norm,
                                2,
                                16,
                                batch_norm=None,
                                downsample_strides=1,
                                weight_decay=0.05)
    #net = residual_block(net, 5, 16)
    #net = residual_block(net, 1, 32, )
    #net = residual_block(net, 4, 32)
    #net = residual_block(net, 1, 64, downsample=True)
    #net = residual_block(net, 2, 64)
    net = tflearn.batch_normalization(net)
    net = tflearn.activation(net, 'relu')
    '''
    conv3=conv_3d(batch_norm,24,[24,1,1],padding='VALID',strides=[1,5,1,1,1],activation='prelu')
    print('conv3', conv3.get_shape().as_list())
    batch_norm = batch_normalization(conv3)
    #act=tflearn.activations.relu(batch_norm)
    #pool3=max_pool_3d(act,[1,1,2,2,1],strides=[1,1,1,1,1])
    '''

    flat = flatten(net)
    print('flat', flat.get_shape().as_list())
    ip1 = fully_connected(
        flat,
        100,
        activation='prelu',
    )
    dro = dropout(ip1, 0.9)
    ip2 = fully_connected(
        dro,
        20,
        activation='softmax',
    )
    network = regression(ip2,
                         optimizer='Adagrad',
                         loss='categorical_crossentropy',
                         learning_rate=0.01)

    model = tflearn.DNN(network,
                        tensorboard_verbose=0,
                        tensorboard_dir="./tflearn_logs/")
    model.fit(x_train,
              y_train,
              n_epoch=200,
              shuffle=True,
              validation_set=(x_test, y_test),
              show_metric=True,
              batch_size=32,
              run_id='3d_net')
                  3,
                  activation='relu',
                  regularizer="L2",
                  weight_decay=0.0001)
network = max_pool_2d(network, 2)

network = conv_2d(network,
                  16,
                  5,
                  activation='relu',
                  regularizer="L2",
                  weight_decay=0.0001)
network = max_pool_2d(network, 2)

# A fully connected layer with regularization (batch normalization and dropout)
network = fully_connected(network, 512, activation='relu')
network = batch_normalization(network)
network = dropout(network, 0.7)

# The climate proxies are processed in a separate "branch" and then merged to the network
cbranch = fully_connected(in_climate, 64, activation='relu', regularizer='L2')
cbranch = fully_connected(cbranch, 64, activation='relu', regularizer='L2')
cbranch = fully_connected(cbranch, 64, activation='relu', regularizer='L2')
network = merge([network, cbranch],
                'concat')  # merge the climate input into the network

# A series of fully connected layers:
network = fully_connected(network, 512, activation='relu')
network = batch_normalization(network)
network = dropout(network, 0.7)
test_img = np.asarray(test_img, dtype=np.int64)

CNN = input_data(shape=[None, 224, 224, 3], name="input_x")

CNN = conv_2d(CNN, 32, 7, activation='relu', regularizer="L2")
CNN = avg_pool_2d(CNN, 2)
CNN = dropout(CNN, keep_prob=0.5)

CNN = conv_2d(CNN, 45, 5, activation='relu', regularizer="L2")
CNN = avg_pool_2d(CNN, 2)
CNN = dropout(CNN, keep_prob=0.5)

CNN = conv_2d(CNN, 10, 2, activation='relu', regularizer='L2')
CNN = avg_pool_2d(CNN, 2)

fl = fully_connected(CNN, 1, activation='softmax')
output = regression(fl,
                    learning_rate=0.0005,
                    loss='binary_crossentropy',
                    name='targets')

model = tflearn.DNN(output,
                    tensorboard_verbose=0,
                    tensorboard_dir='./walk_run',
                    checkpoint_path='./walk_run/checkpoint')
model.fit({'input_x': train_img}, {'targets': train_label},
          show_metric=True,
          n_epoch=20,
          batch_size=600)
model.evaluate({'input_x': test_img}, {'targets': test_label})
Пример #58
0
# Real-time data preprocessing
img_prep = ImagePreprocessing()
img_prep.add_featurewise_zero_center()
img_prep.add_featurewise_stdnorm()

# Real-time data augmentation
img_aug = ImageAugmentation()
img_aug.add_random_flip_leftright()
img_aug.add_random_flip_updown()
img_aug.add_random_rotation(max_angle=25.)

# Convolutional network building
network = input_data(shape=[None, 80, 80, 3],
                     data_preprocessing=img_prep,
                     data_augmentation=img_aug)
network = conv_2d(network, 32, 3, activation='relu')
network = max_pool_2d(network, 2)
network = conv_2d(network, 64, 3, activation='relu')
network = conv_2d(network, 64, 3, activation='relu')
network = max_pool_2d(network, 2)
network = fully_connected(network, 512, activation='relu')
network = dropout(network, 0.5)
network = fully_connected(network, 2, activation='softmax')
network = regression(network,
                     optimizer='adam',
                     loss='categorical_crossentropy',
                     learning_rate=0.001)

# Define model
model = tflearn.DNN(network, tensorboard_verbose=0)