<jupyter_start><jupyter_text>Neural Network Created From Scratch Without Using built-in tensorflow models<jupyter_code>def layer_sizes(X, Y):
    n_x = X.shape[0]
    n_h = 4
    n_y = Y.shape[0]
    return (n_x, n_h, n_y)
def initialize_parameters(n_x, n_h, n_y):
    np.random.seed(2)
    W1 = np.random.randn(n_h, n_x)*0.01
    b1 = np.zeros((n_h, 1))
    W2 = np.random.randn(n_y, n_h)*0.01
    b2 = np.zeros((n_y, 1))
    
    parameters = {"W1": W1,
                  "b1": b1,
                  "W2": W2,
                  "b2": b2}
    
    return parameters
def forward_propagation(X, parameters):
    W1 = parameters["W1"]
    b1 = parameters["b1"]
    W2 = parameters["W2"]
    b2 = parameters["b2"]
    
    Z1 = np.dot(W1,X) + b1
    A1 = np.tanh(Z1)
    Z2 = np.dot(W2,A1) + b2
    A2 = sigmoid(Z2)
    
    cache = {"Z1": Z1,
             "A1": A1,
             "Z2": Z2,
             "A2": A2}
    
    return A2, cache
def compute_cost(A2, Y, parameters):
    
    m = Y.shape[1]
    logprobs = -(1/m)*(np.multiply(np.log(A2),Y) + np.multiply(np.log(1-A2),1-Y))
    cost = np.sum(logprobs)
    cost = np.squeeze(cost)         
    return cost
def backward_propagation(parameters, cache, X, Y):
    m = X.shape[1]
    W1 = parameters["W1"]
    W2 = parameters["W2"]
    A1 = cache["A1"]
    A2 = cache["A2"]

    dZ2 = A2 - Y
    dW2 = (1/m)*np.dot(dZ2, A1.T)
    db2 = (1/m)*np.sum(dZ2,axis=1,keepdims=True)
    dZ1 = (np.dot(W2.T,dZ2))*(1-np.power(A1,2))
    dW1 = (1/m)*np.dot(dZ1,X.T)
    db1 = (1/m)*np.sum(dZ1,axis=1,keepdims=True)
    
    grads = {"dW1": dW1,
             "db1": db1,
             "dW2": dW2,
             "db2": db2}
    
    return grads
def update_parameters(parameters, grads, learning_rate = 1.2):
    W1 = parameters["W1"]
    b1 = parameters["b1"]
    W2 = parameters["W2"]
    b2 = parameters["b2"]

    dW1 = grads["dW1"]
    db1 = grads["db1"]
    dW2 = grads["dW2"]
    db2 = grads["db2"]

    W1 = W1 - learning_rate*dW1
    b1 = b1 - learning_rate*db1
    W2 = W2 - learning_rate*dW2
    b2 = b2 - learning_rate*db2
    
    parameters = {"W1": W1,
                  "b1": b1,
                  "W2": W2,
                  "b2": b2}
    
    return parameters
def nn_model(X, Y, n_h, num_iterations = 10000, print_cost=False):
    np.random.seed(3)
    n_x = layer_sizes(X, Y)[0]
    n_y = layer_sizes(X, Y)[2]
    
    parameters = initialize_parameters(n_x, n_h, n_y)
    W1 = parameters["W1"]
    b1 = parameters["b1"]
    W2 = parameters["W2"]
    b2 = parameters["b2"]

    for i in range(0, num_iterations):
        A2, cache = forward_propagation(X, parameters)
        cost = compute_cost(A2, Y, parameters)
        grads = backward_propagation(parameters, cache, X, Y)
        parameters = update_parameters(parameters, grads, learning_rate = 1.2)
        if print_cost and i % 3000 == 0:
            print ("Cost after iteration %i: %f" %(i, cost))

    return parameters
def predict(parameters, X):
    A2, cache = forward_propagation(X, parameters)
    predictions = (A2 > 0.5)
    return predictions
parameters = nn_model(X, Y, n_h = 4, num_iterations = 10000, print_cost=True)

plot_decision_boundary(lambda x: predict(parameters, x.T), X, Y[0,:])
plt.title("Decision Boundary for hidden layer size " + str(4))
plt.figure(figsize=(16, 32))
hidden_layer_sizes = [1, 2, 3, 4, 5, 20, 50, 100]
for i, n_h in enumerate(hidden_layer_sizes):
    plt.subplot(5, 2, i+1)
    plt.title('Hidden Layer of size %d' % n_h)
    parameters = nn_model(X, Y, n_h, num_iterations = 5000)
    plot_decision_boundary(lambda x: predict(parameters, x.T), X, Y[0,:])
    predictions = predict(parameters, X)
    accuracy = float((np.dot(Y,predictions.T) + np.dot(1-Y,1-predictions.T))/float(Y.size)*100)
    print ("Accuracy for {} hidden units: {} %".format(n_h, accuracy))<jupyter_output>Accuracy for 1 hidden units: 67.5 %
Accuracy for 2 hidden units: 67.25 %
Accuracy for 3 hidden units: 90.75 %
Accuracy for 4 hidden units: 90.5 %
Accuracy for 5 hidden units: 91.25 %
Accuracy for 20 hidden units: 90.5 %
Accuracy for 50 hidden units: 90.75 %
Accuracy for 100 hidden units: 91.0 %
StarcoderdataJupyterStructuredCleanDedup