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diff --git a/2 - Modelling data with multiple channels/pixelCNN_RGB.py b/2 - Modelling data with multiple channels/pixelCNN_RGB.py
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+
+"""Script to train pixelCNN on the CIFAR10 dataset."""
+import random as rn
+import time
+
+import matplotlib
+import matplotlib.pyplot as plt
+import numpy as np
+import tensorflow as tf
+from tensorflow import keras
+
+
+class MaskedConv2D(keras.layers.Layer):
+    """Convolutional layers with masks.
+    Convolutional layers with simple implementation of masks type A and B for
+    autoregressive models.
+    Arguments:
+    mask_type: one of `"A"` or `"B".`
+    filters: Integer, the dimensionality of the output space
+        (i.e. the number of output filters in the convolution).
+    kernel_size: An integer or tuple/list of 2 integers, specifying the
+        height and width of the 2D convolution window.
+        Can be a single integer to specify the same value for
+        all spatial dimensions.
+    strides: An integer or tuple/list of 2 integers,
+        specifying the strides of the convolution along the height and width.
+        Can be a single integer to specify the same value for
+        all spatial dimensions.
+        Specifying any stride value != 1 is incompatible with specifying
+        any `dilation_rate` value != 1.
+    padding: one of `"valid"` or `"same"` (case-insensitive).
+    kernel_initializer: Initializer for the `kernel` weights matrix.
+    bias_initializer: Initializer for the bias vector.
+    """
+
+    def __init__(self,
+                 mask_type,
+                 filters,
+                 kernel_size,
+                 strides=1,
+                 padding='same',
+                 kernel_initializer='glorot_uniform',
+                 bias_initializer='zeros',
+                 input_n_channels=3):
+        super(MaskedConv2D, self).__init__()
+
+        assert mask_type in {'A', 'B'}
+        self.mask_type = mask_type
+
+        self.filters = filters
+        self.kernel_size = kernel_size
+        self.strides = strides
+        self.padding = padding.upper()
+        self.kernel_initializer = keras.initializers.get(kernel_initializer)
+        self.bias_initializer = keras.initializers.get(bias_initializer)
+        self.input_n_channels = input_n_channels
+
+    def build(self, input_shape):
+        self.kernel = self.add_weight("kernel",
+                                      shape=(self.kernel_size,
+                                             self.kernel_size,
+                                             int(input_shape[-1]),
+                                             self.filters),
+                                      initializer=self.kernel_initializer,
+                                      trainable=True)
+
+        self.bias = self.add_weight("bias",
+                                    shape=(self.filters,),
+                                    initializer=self.bias_initializer,
+                                    trainable=True)
+        center = self.kernel_size // 2
+        mask = np.ones(self.kernel.shape, dtype=np.float32)
+        mask[center, center + 1:, :, :] = 0
+        mask[center + 1:, :, :, :] = 0
+
+        for i in range(self.input_n_channels):
+            for j in range(self.input_n_channels):
+                if (self.mask_type == 'A' and i >= j) or (self.mask_type == 'B' and i > j):
+                    mask[center, center, i::self.input_n_channels, j::self.input_n_channels] = 0
+
+        self.mask = tf.constant(mask, dtype=tf.float32, name='mask')
+
+    def call(self, input):
+        masked_kernel = tf.math.multiply(self.mask, self.kernel)
+        x = tf.nn.conv2d(input,
+                         masked_kernel,
+                         strides=[1, self.strides, self.strides, 1],
+                         padding=self.padding)
+        x = tf.nn.bias_add(x, self.bias)
+        return x
+
+
+class ResidualBlock(keras.Model):
+    """Residual blocks that compose pixelCNN
+    Blocks of layers with 3 convolutional layers and one residual connection.
+    Based on Figure 5 from [1] where h indicates number of filters.
+    Refs:
+    [1] - Oord, A. V. D., Kalchbrenner, N., & Kavukcuoglu, K. (2016). Pixel recurrent
+    neural networks. arXiv preprint arXiv:1601.06759.
+    """
+
+    def __init__(self, h):
+        super(ResidualBlock, self).__init__(name='')
+
+        self.conv2a = MaskedConv2D(mask_type='B', filters=h//2, kernel_size=1, strides=1)
+        self.conv2b = MaskedConv2D(mask_type='B', filters=h//2, kernel_size=7, strides=1)
+        self.conv2c = MaskedConv2D(mask_type='B', filters=h, kernel_size=1, strides=1)
+
+    def call(self, input_tensor):
+        x = tf.nn.relu(input_tensor)
+        x = self.conv2a(x)
+
+        x = tf.nn.relu(x)
+        x = self.conv2b(x)
+
+        x = tf.nn.relu(x)
+        x = self.conv2c(x)
+
+        x += input_tensor
+        return x
+
+
+def quantise(images, q_levels):
+    """Quantise image into q levels."""
+    return (np.digitize(images, np.arange(q_levels) / q_levels) - 1).astype('float32')
+
+
+def main():
+    # ------------------------------------------------------------------------------------
+    # Defining random seeds
+    random_seed = 42
+    tf.random.set_seed(random_seed)
+    np.random.seed(random_seed)
+    rn.seed(random_seed)
+
+    # ------------------------------------------------------------------------------------
+# Loading data
+(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()
+
+    height = 32
+    width = 32
+    n_channel = 3
+
+    x_train = x_train.astype('float32') / 255.
+    x_test = x_test.astype('float32') / 255.
+
+    x_train = x_train.reshape(x_train.shape[0], height, width, n_channel)
+    x_test = x_test.reshape(x_test.shape[0], height, width, n_channel)
+
+    # --------------------------------------------------------------------------------------------------------------
+    # Quantisize the input data in q levels
+    q_levels = 8
+    x_train_quantised_of = quantisize(x_train_overfit, q_levels)
+    x_test_quantised_of = quantisize(x_test_overfit, q_levels)
+
+    # ------------------------------------------------------------------------------------
+    # Creating input stream using tf.data API
+    batch_size = 128
+    train_buf = 60000
+
+    train_dataset = tf.data.Dataset.from_tensor_slices(
+        (x_train_quantised / (q_levels - 1),
+         x_train_quantised.astype('int32')))
+    train_dataset = train_dataset.shuffle(buffer_size=train_buf)
+    train_dataset = train_dataset.batch(batch_size)
+
+    test_dataset = tf.data.Dataset.from_tensor_slices((x_test_quantised / (q_levels - 1),
+                                                       x_test_quantised.astype('int32')))
+    test_dataset = test_dataset.batch(batch_size)
+
+    # --------------------------------------------------------------------------------------------------------------
+    # Create PixelCNN model
+    n_filters = 120 
+    inputs = keras.layers.Input(shape=(height, width, n_channel))
+    x = MaskedConv2D(mask_type='A', filters=n_filters, kernel_size=7)(inputs)
+
+    for i in range(15):
+        x = keras.layers.Activation(activation='relu')(x)
+        x = ResidualBlock(h=n_filters)(x)
+
+    x = keras.layers.Activation(activation='relu')(x)
+    x = MaskedConv2D(mask_type='B', filters=n_filters, kernel_size=1)(x) 
+    x = keras.layers.Activation(activation='relu')(x)
+    x = MaskedConv2D(mask_type='B', filters=n_channel * q_levels, kernel_size=1)(x)  # shape [N,H,W,DC]
+
+    pixelcnn = tf.keras.Model(inputs=inputs, outputs=x)
+
+    # --------------------------------------------------------------------------------------------------------------
+    # Prepare optimizer and loss function
+    lr_decay = 0.9999
+    learning_rate = 5e-3 #5
+    optimizer = tf.keras.optimizers.Adam(lr=learning_rate)
+
+    compute_loss = tf.keras.losses.CategoricalCrossentropy(from_logits=True)
+
+    # --------------------------------------------------------------------------------------------------------------
+    @tf.function
+    def train_step(batch_x, batch_y):
+        with tf.GradientTape() as ae_tape:
+            logits = pixelcnn(batch_x, training=True)
+
+            logits = tf.reshape(logits, [-1, height, width, q_levels, n_channel])
+            logits = tf.transpose(logits, perm=[0, 1, 2, 4, 3])
+
+            loss = compute_loss(tf.one_hot(batch_y, q_levels), logits)
+
+        gradients = ae_tape.gradient(loss, pixelcnn.trainable_variables)
+        gradients, _ = tf.clip_by_global_norm(gradients, 1.0)
+        optimizer.apply_gradients(zip(gradients, pixelcnn.trainable_variables))
+
+        return loss
+
+    # ------------------------------------------------------------------------------------
+    # Training loop
+    n_epochs = 20
+    n_iter = int(np.ceil(x_train_quantised.shape[0] / batch_size))
+    for epoch in range(n_epochs):
+        progbar = Progbar(n_iter)
+        print('Epoch {:}/{:}'.format(epoch + 1, n_epochs))
+
+        for i_iter, (batch_x, batch_y) in enumerate(train_dataset):
+            optimizer.lr = optimizer.lr * lr_decay
+            loss = train_step(batch_x, batch_y)
+
+            progbar.add(1, values=[('loss', loss)])
+
+    # ------------------------------------------------------------------------------------
+    # Test set performance
+    test_loss = []
+    for batch_x, batch_y in test_dataset:
+        logits = pixelcnn(batch_x, training=False)
+
+        # Calculate cross-entropy (= negative log-likelihood)
+        loss = compute_loss(tf.squeeze(tf.one_hot(batch_y, q_levels)), logits)
+
+        test_loss.append(loss)
+    print('nll : {:} nats'.format(np.array(test_loss).mean()))
+    print('bits/dim : {:}'.format(np.array(test_loss).mean() / np.log(2)))
+
+    # ------------------------------------------------------------------------------------
+    # Generating new images
+    samples = np.zeros((100, height, width, n_channel), dtype='float32')
+    for i in range(height):
+        for j in range(width):
+            logits = pixelcnn(samples)
+            next_sample = tf.random.categorical(logits[:, i, j, :], 1)
+            samples[:, i, j, 0] = (next_sample.numpy() / (q_levels - 1))[:, 0]
+
+    fig = plt.figure(figsize=(10, 10))
+    for i in range(100):
+        ax = fig.add_subplot(10, 10, i + 1)
+        ax.matshow(samples[i, :, :, 0], cmap=matplotlib.cm.binary)
+        plt.xticks(np.array([]))
+        plt.yticks(np.array([]))
+    plt.show()
+
+    # ------------------------------------------------------------------------------------
+    # Filling occluded images
+    occlude_start_row = 16
+    num_generated_images = 10
+    samples = np.copy(x_test_quantised[0:num_generated_images, :, :, :])
+    samples = samples / (q_levels - 1)
+    samples[:, occlude_start_row:, :, :] = 0
+
+    fig = plt.figure(figsize=(10, 10))
+
+    for i in range(10):
+        ax = fig.add_subplot(1, 10, i + 1)
+        ax.matshow(samples[i, :, :, 0], cmap=matplotlib.cm.binary)
+        plt.xticks(np.array([]))
+        plt.yticks(np.array([]))
+
+    for i in range(occlude_start_row, height):
+        for j in range(width):
+            for k in range(n_channel):
+                logits = pixelcnn(samples)
+                logits = tf.reshape(logits, [-1, height, width, q_levels, n_channel])
+                logits = tf.transpose(logits, perm=[0, 1, 2, 4, 3])
+                next_sample = tf.random.categorical(logits[:, i, j, k, :], 1)
+                samples[:, i, j, k] = (next_sample.numpy() / (q_levels - 1))[:, 0]
+    fig = plt.figure(figsize=(10, 10))
+
+    for i in range(10):
+        ax = fig.add_subplot(1, 10, i + 1)
+        ax.matshow(samples[i, :, :, 0], cmap=matplotlib.cm.binary)
+        plt.xticks(np.array([]))
+        plt.yticks(np.array([]))
+    plt.show()
+
+
+if __name__ == '__main__':
+main()
+