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

 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()

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 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

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')

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

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 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')

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')

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)

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)

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)

    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

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)

 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

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

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