human
Solve the following python programming problem given the following code:
# Helper Functions

def convolution_layer(inputs, kernel_shape, stride_shape, output_depth):
    # Convolution variables
    input_depth = inputs.get_shape().as_list()[3]
    filter_shape = kernel_shape + (input_depth, output_depth)
    dev = 1/np.sqrt(kernel_shape[0]*kernel_shape[1])
    filter_ = tf.Variable(tf.truncated_normal(filter_shape, stddev=dev), name="filter_")
    stride_shape = (1,) + stride_shape + (1,)
    pool_shape = stride_shape
    bias_ = tf.Variable(tf.truncated_normal([output_depth], stddev=dev), name="bias_")
    
    # Convolution
    x = tf.nn.conv2d(inputs, filter_, stride_shape, padding='SAME')
    x = tf.nn.bias_add(x, bias_)
    x = tf.nn.relu(x)
    x = tf.nn.max_pool(x, pool_shape, stride_shape, padding='SAME')
    
    return x

def dense_layer(inputs, num_outputs):
    # Dense variables
    num_inputs = inputs.get_shape().as_list()[1]
    dev = 1/np.sqrt(num_inputs)
    weights = tf.Variable(tf.truncated_normal((num_inputs,)+num_outputs, stddev=dev), name="weights")
    bias = tf.Variable(tf.truncated_normal(num_outputs, stddev=dev), name="bias")
    
    return tf.add(tf.matmul(inputs, weights), bias)

class SimpsonsClassifier:

    def __init__(self, dim_size, num_classes):
        # Shape
        self.image_shape = (dim_size, dim_size, 3)
        self.label_shape = (num_classes,)

        # Data
        self.X = tf.placeholder(tf.float32, (None,) + self.image_shape)
        self.y = tf.placeholder(tf.float32, (None,) + self.label_shape)

        # Model
        self.logits = None
        self.cost = None
        self.optimizer = None
        self.accuracy = None
        self.__build__()

    def __build__(self):
        # Convolutions
        print("Inputs shape: {}".format(self.X.get_shape().as_list()[1:]))

        conv = convolution_layer(self.X, (5,5), (2,2), 32)
        print("Convlution_1 layer shape: {}".format(conv.get_shape().as_list()[1:]))
        
        conv = convolution_layer(conv, (5,5), (2,2), 64)
        print("Convolution_2 layer shape:...
Given the above code, generate some markdown instructions for it.
assistant
#Here is the Python program you requested.

* Note:
At this point the training images are already clean, rescaled and stored in pickles files.
Building the Model