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In this post I will be explaining how to design your own Neural Network (Keras-like) framework from scratch in python 3.7+. This post assumes you have basic understanding of how a Neural Network works and familiarity with forward-backward propagation, Loss functions, Optimizers, Regularization and so on. Without any further due, let’s jump right into it.

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A basic neural network model implementation in Keras framework looks like the following. In our implementation from scratch, we will try to design a similar framework as Keras (but basic).

# define the keras model
model = Sequential()
model.add(Dense(12, input_dim=8, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X, y, epochs=150, batch_size=10)

Base Neural Network class

In order to keep all the functional elements together we will create a class (Vanilla here) to organize the functionality of our Neural Network. The class can store variables to define how the network in structured and parameterized.

class Vanilla:
    def __init__(self):
        self.layers = []
        self.activations = []
        self.alpha_0 = 0.01
        self.loss_fn = "entropy"
        self.iterations = 0
        self.dict = {}
        self.decay_rate = 0.001
        self.problem_type = "c" # c for classification & r for regression

Adding Layers

This function will allow users to add layers and design the graph of the network. Note that this function stores the number of hidden units in a variable named layers (similarly activation).

def add_layer(self, units, input_dim=0, activation="sigmoid"):
  if input_dim == 0:

Compile Function

This function initializes various model parameters before starting the training in the next step.

def compile(self, learning_rate=0.01, decay_rate=0.001, loss="entropy"):
        self.alpha_0 = learning_rate
        self.loss_fn = loss
        self.decay_rate = decay_rate

Training the network

The fit method lies at the heart of this implementation. Although the actual function has a vast functionality, we will look at the most important part which is forward and backpropagation.

# Forward Pass
for i in range(no_of_layers):
        a[i], self.dict['W' + str(i+1)], self.dict['b' + str(i+1)], activation=self.activations[i]))

dz = []
dW = []
db = []

# Backpropagation of Gradients
for i in range(no_of_layers, 0, -1):
    if i == no_of_layers:
        [dzz, dWW, dbb] = self.backward(
            a[i-1], a[i], [], dz[no_of_layers-i], activation=self.activations[i-1], output_layer=True)
        [dzz, dWW, dbb] = self.backward(
            a[i-1], a[i], self.dict['W' + str(i+1)], dz[no_of_layers-i], activation=self.activations[i-1])


# Update Weights and Biases
for i in range(1, no_of_layers + 1):
    if regularize:
        dW[no_of_layers -
            i] += self.l1_reg(self.dict['W' + str(i)], lambda_)

    self.dict['W' + str(i)] -= (alpha * dW[no_of_layers-i])
    self.dict['b' + str(i)] -= (alpha * db[no_of_layers-i])

Activation Functions

There are several activation functions (relu, sigmoid, softmax, tanh) defined in original Vanilla. An example of sigmoid and it’s derivative is shown below.

def sigmoid(self, x):
    return (1.0/(1.0+np.exp(-x)))

def d_sigmoid(self, x):
    a = self.sigmoid(x)
    return a * (1 - a)

Loss Functions

Vanilla class contains three different loss functions — Cross Entropy, Logistic Loss, Mean Squared Error (MSE). An example implementation of Cross entropy is shown below.

def cross_entropy_loss(self, y_train, y_hat, epsilon=1e-11):
      m = y_train.shape[0]
      n = y_train.shape[1]

      y_hat_clip = np.clip(y_hat, epsilon, 1 - epsilon)
      result = ((-1.0 / (m)) * np.sum(np.sum(y_train *
                np.log(y_hat_clip), axis=1), axis=0))

      if (np.any(np.isnan(result))):
          print("Error in Cross Entropy")
      return result

Predicting the output

The predict method takes the input data (X) and performs a forward pass through the network in order to get prediction values (y).

def predict(self, X):
      no_of_layers = len(self.layers) - 1
      a = []

      for i in range(no_of_layers):
          a.append(self.forward(a[i], self.dict['W' + str(i+1)],
                   self.dict['b' + str(i+1)], activation=self.activations[i]))
      return a[-1]

This is a developing post and more details will be added soon. If you found this post helpful, follow me and share this post!

Github Link: Vanilla Neural Network Repository