# Neural Network Using Python and Numpy Understanding neural networks using Python and Numpy by coding

### Motivation

If you are a junior data scientist who sort of understands how neural nets work, or a machine learning enthusiast who only knows a little about deep learning, this is the article that you cannot miss. Here is how you can build a neural net from scratch using NumPy in 9 steps — from data pre-processing to back-propagation — a must-do practice.

Basic understanding of machine learning, artificial neural network, Python syntax, and programming logic is preferred (but not necessary as you can learn on the go).

Codes are available on Github.

## 1. Initialization Numpy

Step one. Import NumPy. Seriously.

```import numpy as np
np.random.seed(42) # for reproducibility
```

## 2. Data Generation Deep learning is data-hungry. Although there are many clean datasets available online, we will generate our own for simplicity — for inputs a and b, we have outputs a+b, a-b, and |a-b|. 10,000 datum points are generated.

```X_num_row, X_num_col = [2, 10000] # Row is no. of feature, col is no. of datum points
X_raw = np.random.rand(X_num_row,X_num_col) * 100
y_raw = np.concatenate(([(X_raw[0,:] + X_raw[1,:])], [(X_raw[0,:] - X_raw[1,:])], np.abs([(X_raw[0,:] - X_raw[1,:])])))
# for input a and b, output is a+b; a-b and |a-b|
y_num_row, y_num_col = y_raw.shape
```

## 3. Train-test Splitting

Our dataset is split into training (70%) and testing (30%) set. Only training set is leveraged for tuning neural networks. Testing set is used only for performance evaluation when the training is complete.

```train_ratio = 0.7
num_train_datum = int(train_ratio*X_num_col)
X_raw_train = X_raw[:,0:num_train_datum]
X_raw_test = X_raw[:,num_train_datum:]
y_raw_train = y_raw[:,0:num_train_datum]
y_raw_test = y_raw[:,num_train_datum:]
```

## 4. Data Standardization

Data in the training set is standardized so that the distribution for each standardized feature is zero-mean and unit-variance. The scalers generated from the abovementioned procedure can then be applied to the testing set.

```class scaler:
def __init__(self, mean, std):
self.mean = mean
self.std = std

def get_scaler(row):
mean = np.mean(row)
std = np.std(row)
return scaler(mean, std)
def standardize(data, scaler):
return (data - scaler.mean) / scaler.std
def unstandardize(data, scaler):
return (data * scaler.std) + scaler.mean
Construct scalers from training set
X_scalers = [get_scaler(X_raw_train[row,:]) for row in range(X_num_row)]
X_train = np.array([standardize(X_raw_train[row,:], X_scalers[row]) for row in range(X_num_row)])
y_scalers = [get_scaler(y_raw_train[row,:]) for row in range(y_num_row)]
y_train = np.array([standardize(y_raw_train[row,:], y_scalers[row]) for row in range(y_num_row)])
Apply those scalers to testing set
X_test = np.array([standardize(X_raw_test[row,:], X_scalers[row]) for row in range(X_num_row)])
y_test = np.array([standardize(y_raw_test[row,:], y_scalers[row]) for row in range(y_num_row)])
Check if data has been standardized
print([X_train[row,:].mean() for row in range(X_num_row)]) # should be close to zero
print([X_train[row,:].std() for row in range(X_num_row)])  # should be close to one
print([y_train[row,:].mean() for row in range(y_num_row)]) # should be close to zero
print([y_train[row,:].std() for row in range(y_num_row)])  # should be close to one
```

The scaler therefore does not contain any information from our testing set. We do not want our neural net to gain any information regarding testing set before network tuning.

We have now completed the data pre-processing procedures in 4 steps.

### 5. Neural Net Construction Photo by freestocks.org on Unsplash

We objectify a ‘layer’ using class in Python. Every layer (except the input layer) has a weight matrix W, a bias vector b, and an activation function. Each layer is appended to a list called neural_net. That list would then be a representation of your fully connected neural network.

```class layer:
def init(self, layer_index, is_output, input_dim, output_dim, activation):
self.layer_index = layer_index # zero indicates input layer
self.is_output = is_output # true indicates output layer, false otherwise
self.input_dim = input_dim
self.output_dim = output_dim
self.activation = activation
```    # the multiplication constant is sorta arbitrary
if layer_index != 0:
self.W = np.random.randn(output_dim, input_dim) * np.sqrt(2/input_dim)
self.b = np.random.randn(output_dim, 1) * np.sqrt(2/input_dim)```
Change layers_dim to configure your own neural net!
layers_dim = [X_num_row, 4, 4, y_num_row] # input layer --- hidden layers --- output layers
neural_net = []
Construct the net layer by layer
for layer_index in range(len(layers_dim)):
if layer_index == 0: # if input layer
neural_net.append(layer(layer_index, False, 0, layers_dim[layer_index], 'irrelevant'))
elif layer_index+1 == len(layers_dim): # if output layer
neural_net.append(layer(layer_index, True, layers_dim[layer_index-1], layers_dim[layer_index], activation='linear'))
else:
neural_net.append(layer(layer_index, False, layers_dim[layer_index-1], layers_dim[layer_index], activation='relu'))
Simple check on overfitting
pred_n_param = sum([(layers_dim[layer_index]+1)*layers_dim[layer_index+1] for layer_index in range(len(layers_dim)-1)])
act_n_param = sum([neural_net[layer_index].W.size + neural_net[layer_index].b.size for layer_index in range(1,len(layers_dim))])
print(f'Predicted number of hyperparameters: {pred_n_param}')
print(f'Actual number of hyperparameters: {act_n_param}')
print(f'Number of data: {X_num_col}')
if act_n_param >= X_num_col:
raise Exception('It will overfit.')
```

Finally, we do a sanity check on the number of hyperparameters using the following formula, and by counting. The number of datums available should exceed the number of hyperparameters, otherwise it will definitely overfit. N^l is number of hyperparameters at l-th layer, L is number of layers (excluding input layer)

## 6. Forward Propagation

We define a function for forward propagation given a certain set of weights and biases. The connection between layers is defined in matrix form as: σ is element-wise activation function, superscript T means transpose of a matrix

Activation functions are defined one by one. ReLU is implemented as a → max(a,0), whereas sigmoid function should return a → 1/(1+e^(-a)), and its implementation is left as an exercise to the reader.

```def activation(input_, act_func):
if act_func == 'relu':
return np.maximum(input_, np.zeros(input_.shape))
elif act_func == 'linear':
return input_
else:
raise Exception('Activation function is not defined.')
def forward_prop(input_vec, layers_dim=layers_dim, neural_net=neural_net):
neural_net.A = input_vec # Define A in input layer for for-loop convenience
for layer_index in range(1,len(layers_dim)): # W,b,Z,A are undefined in input layer
neural_net[layer_index].A = activation(neural_net[layer_index].Z, neural_net[layer_index].activation)
return neural_net[layer_index].A``` Photo by Holger Link on Unsplash

### 7. Back-propagation

This is the most tricky part where many of us simply do not understand. Once we have defined a loss metric e for evaluating performance, we would like to know how the loss metric change when we perturb each weight or bias.

We want to know how sensitive each weight and bias is with respect to the loss metric.

This is represented by partial derivatives ∂e/∂W (denoted dW in code) and ∂e/∂b (denoted db in code) respectively, and can be calculated analytically. ⊙ represents element-wise multiplication  These back-propagation equations assume only one datum y is compared. The gradient update process would be very noisy as the performance of each iteration is subject to one datum point only. Multiple datums can be used to reduce the noise where ∂W(y1, y2, …) would be the mean of ∂W(y_1), ∂W(y_2), …, and likewise for ∂b. This is not shown above in those equations, but is implemented in the code below.

```def get_loss(y, y_hat, metric='mse'):
if metric == 'mse':
individual_loss = 0.5 * (y_hat - y) ** 2
return np.mean([np.linalg.norm(individual_loss[:,col], 2) for col in range(individual_loss.shape)])
else:
raise Exception('Loss metric is not defined.')
def get_dZ_from_loss(y, y_hat, metric):
if metric == 'mse':
return y_hat - y
else:
raise Exception('Loss metric is not defined.')
def get_dactivation(A, act_func):
if act_func == 'relu':
return np.maximum(np.sign(A), np.zeros(A.shape)) # 1 if backward input >0, 0 otherwise; then diaganolize
elif act_func == 'linear':
return np.ones(A.shape)
else:
raise Exception('Activation function is not defined.')
def backward_prop(y, y_hat, metric='mse', layers_dim=layers_dim, neural_net=neural_net, num_train_datum=num_train_datum):
for layer_index in range(len(layers_dim)-1,0,-1):
if layer_index+1 == len(layers_dim): # if output layer
dZ = get_dZ_from_loss(y, y_hat, metric)
else:
dZ = np.multiply(np.dot(neural_net[layer_index+1].W.T, dZ),
get_dactivation(neural_net[layer_index].A, neural_net[layer_index].activation))
dW = np.dot(dZ, neural_net[layer_index-1].A.T) / num_train_datum
db = np.sum(dZ, axis=1, keepdims=True) / num_train_datum
```    neural_net[layer_index].dW = dW
neural_net[layer_index].db = db```
```

### 8. Iterative Optimization

We now have every building block for training a neural network.

Once we know the sensitivities of weights and biases, we try to minimize (hence the minus sign) the loss metric iteratively by gradient descent using the following update rule:

```W = W - learning_rate * ∂W
b = b - learning_rate * ∂b
``` Photo by Rostyslav Savchyn on Unsplash

```learning_rate = 0.01
max_epoch = 1000000
for epoch in range(1,max_epoch+1):
y_hat_train = forward_prop(X_train) # update y_hat
backward_prop(y_train, y_hat_train) # update (dW,db)
```for layer_index in range(1,len(layers_dim)):        # update (W,b)
neural_net[layer_index].W = neural_net[layer_index].W - learning_rate * neural_net[layer_index].dW
neural_net[layer_index].b = neural_net[layer_index].b - learning_rate * neural_net[layer_index].db

if epoch % 100000 == 0:
print(f'{get_loss(y_train, y_hat_train):.4f}')```
```

Training loss should be going down as it iterates

## 9. Testing

The model generalizes well if the testing loss is not much higher than the training loss. We also make some test cases to see how the model performs.

```# test loss
print(get_loss(y_test, forward_prop(X_test)))
def predict(X_raw_any):
X_any = np.array([standardize(X_raw_any[row,:], X_scalers[row]) for row in range(X_num_row)])
y_hat = forward_prop(X_any)
y_hat_any = np.array([unstandardize(y_hat[row,:], y_scalers[row]) for row in range(y_num_row)])
return y_hat_any
predict(np.array([[30,70],[70,30],[3,5],[888,122]]).T)
```

### The Takeaway

This is how you can build a neural net from scratch using NumPy in 9 steps. Some of you might have already built neural nets using some high-level frameworks such as TensorFlow, PyTorch, or Keras. However, building a neural net using only low-level libraries enable us to truly understand the mathematics behind the mystery.

My implementation by no means is the most efficient way to build and train a neural net. There is so much room for improvement but that is a story for another day. Codes are available on Github. Happy coding!

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## Data Science Projects | Data Science | Machine Learning | Python

Practice your skills in Data Science with Python, by learning and then trying all these hands-on, interactive projects, that I have posted for you.

## Data Science Projects | Data Science | Machine Learning | Python

Practice your skills in Data Science with Python, by learning and then trying all these hands-on, interactive projects, that I have posted for you.

## Data Science Projects | Data Science | Machine Learning | Python

Practice your skills in Data Science with Python, by learning and then trying all these hands-on, interactive projects, that I have posted for you.

## Data Science Projects | Data Science | Machine Learning | Python

Practice your skills in Data Science with Python, by learning and then trying all these hands-on, interactive projects, that I have posted for you.