SkalskiP

def update(params_values, grads_values, nn_architecture, learning_rate):
    for layer_idx, layer in enumerate(nn_architecture):
        params_values["W" + str(layer_idx)] -= learning_rate * grads_values["dW" + str(layer_idx)]        
        params_values["b" + str(layer_idx)] -= learning_rate * grads_values["db" + str(layer_idx)]

    return params_values;

SkalskiP

def full_backward_propagation(Y_hat, Y, memory, params_values, nn_architecture):
    grads_values = {}
    m = Y.shape[1]
    Y = Y.reshape(Y_hat.shape)
   
    dA_prev = - (np.divide(Y, Y_hat) - np.divide(1 - Y, 1 - Y_hat));
    
    for layer_idx_prev, layer in reversed(list(enumerate(nn_architecture))):
        layer_idx_curr = layer_idx_prev + 1
        activ_function_curr = layer["activation"]

SkalskiP

def single_layer_backward_propagation(dA_curr, W_curr, b_curr, Z_curr, A_prev, activation="relu"):
    m = A_prev.shape[1]
    
    if activation is "relu":
        backward_activation_func = relu_backward
    elif activation is "sigmoid":
        backward_activation_func = sigmoid_backward
    else:
        raise Exception('Non-supported activation function')
    

SkalskiP

def get_cost_value(Y_hat, Y):
    m = Y_hat.shape[1]
    cost = -1 / m * (np.dot(Y, np.log(Y_hat).T) + np.dot(1 - Y, np.log(1 - Y_hat).T))
    return np.squeeze(cost)

def get_accuracy_value(Y_hat, Y):
    Y_hat_ = convert_prob_into_class(Y_hat)
    return (Y_hat_ == Y).all(axis=0).mean()

SkalskiP

def full_forward_propagation(X, params_values, nn_architecture):
    memory = {}
    A_curr = X
    
    for idx, layer in enumerate(nn_architecture):
        layer_idx = idx + 1
        A_prev = A_curr
        
        activ_function_curr = layer["activation"]
        W_curr = params_values["W" + str(layer_idx)]

SkalskiP

def single_layer_forward_propagation(A_prev, W_curr, b_curr, activation="relu"):
    Z_curr = np.dot(W_curr, A_prev) + b_curr
    
    if activation is "relu":
        activation_func = relu
    elif activation is "sigmoid":
        activation_func = sigmoid
    else:
        raise Exception('Non-supported activation function')
        

SkalskiP

def sigmoid(Z):
    return 1/(1+np.exp(-Z))

def relu(Z):
    return np.maximum(0,Z)

def sigmoid_backward(dA, Z):
    sig = sigmoid(Z)
    return dA * sig * (1 - sig)

SkalskiP

nn_architecture = [
    {"input_dim": 2, "output_dim": 4, "activation": "relu"},
    {"input_dim": 4, "output_dim": 6, "activation": "relu"},
    {"input_dim": 6, "output_dim": 6, "activation": "relu"},
    {"input_dim": 6, "output_dim": 4, "activation": "relu"},
    {"input_dim": 4, "output_dim": 1, "activation": "sigmoid"},
]

SkalskiP

def init_layers(nn_architecture, seed = 99):
    np.random.seed(seed)
    number_of_layers = len(nn_architecture)
    params_values = {}

    for idx, layer in enumerate(nn_architecture):
        layer_idx = idx + 1
        layer_input_size = layer["input_dim"]
        layer_output_size = layer["output_dim"]
        

SkalskiP

# Adding callback functions that will run on each epoch
testmodelcb = keras.callbacks.LambdaCallback(on_epoch_end=save_model_prediction_graph)
# Compilation of the model
model.compile(loss='binary_crossentropy', optimizer='adamax', metrics=['accuracy'])
# Model training
model.fit(X_train, y_train, epochs=N_EPOCHS, verbose=0, callbacks=[testmodelcb])