# -*- coding: utf-8 -*- 
#
# Author : hhl <dnrhhl@gmail.com>
#
# Time : 二 18  9 2018 09:32:30
#
#
import time
import numpy as np
import h5py
import matplotlib.pyplot as plt
import scipy
from PIL import Image
from scipy import ndimage
from dnn_app_utils_v2 import *

train_x_orig, train_y, test_x_orig, test_y, classes = load_data()
train_x_flatten = train_x_orig.reshape(train_x_orig.shape[0], -1).T   # The "-1" makes reshape flatten the remaining dimensions
test_x_flatten = test_x_orig.reshape(test_x_orig.shape[0], -1).T
# Standardize data to have feature values between 0 and 1.
train_x = train_x_flatten/255.
test_x = test_x_flatten/255.

n_x = 12288     # num_px * num_px * 3
n_h = 7
n_y = 1
layers_dims = (n_x, n_h, n_y)

def two_layer_model(X,Y,layers_dims,learning_rate=0.0075,num_iterations = 3000, print_cost = False):
    """
    Implements a two-layer neural network: LINEAR->RELU->LINEAR->SIGMOID.

    Arguments:
    X -- input data, of shape (n_x, number of examples)
    Y -- true "label" vector (containing 0 if cat, 1 if non-cat), of shape (1, number of examples)
    layers_dims -- dimensions of the layers (n_x, n_h, n_y)
    num_iterations -- number of iterations of the optimization loop
    learning_rate -- learning rate of the gradient descent update rule
    print_cost -- If set to True, this will print the cost every 100 iterations

    Returns:
    parameters -- a dictionary containing W1, W2, b1, and b2
    """

    np.random.seed(1)
    grads = {}
    costs = []
    m = X.shape[1]
    (n_x, n_h, n_y) = layers_dims
    # Initialize parameters dictionary, by calling one of the functions you'd previously implemented
    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):
        # Forward propagation: LINEAR -> RELU -> LINEAR -> SIGMOID. Inputs: "X, W1, b1". Output: "A1, cache1, A2, cache2".
        A1 ,cache1 = linear_activation_forward(X, W1, b1, activation='relu')
        A2 ,cache2 = linear_activation_forward(A1, W2, b2, activation='sigmoid')

        # Compute cost
        cost = compute_cost(A2,Y)
        # Initializing backward propagation
        dA2 = - (np.divide(Y,A2) - np.divide(1 - Y, 1 - A2)) 
        # Backward propagation. Inputs: "dA2, cache2, cache1". Outputs: "dA1, dW2, db2; also dA0 (not used), dW1, db1".
        dA1,dW2,db2 = linear_activation_backward(dA2,cache2,activation='sigmoid')
        dA0,dW1,db1 = linear_activation_backward(dA1,cache1,activation='relu')

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

        parameters = update_parameters(parameters,grads,learning_rate)

        W1 = parameters["W1"]
        b1 = parameters["b1"]
        W2 = parameters["W2"]
        b2 = parameters["b2"]

        if print_cost and i % 100 == 0:
            print("Cost after iteration {}: {}".format(i, np.squeeze(cost)))
        if print_cost and i % 100 == 0:
            costs.append(cost)
    return parameters

#parameters = two_layer_model(train_x, train_y, layers_dims = (n_x, n_h, n_y), num_iterations = 2500, print_cost=True)
#predictions_train = predict(train_x, train_y, parameters)
#predictions_test = predict(test_x, test_y, parameters)

layers_dims = [12288, 20, 7, 5, 1]

def L_layer_model(X, Y, layers_dims, learning_rate = 0.0075 ,num_iterations = 3000, print_cost = False):
    """
    Implements a L-layer neural network: [LINEAR->RELU]*(L-1)->LINEAR->SIGMOID.

    Arguments:
    X -- data, numpy array of shape (number of examples, num_px * num_px * 3)
    Y -- true "label" vector (containing 0 if cat, 1 if non-cat), of shape (1, number of examples)
    layers_dims -- list containing the input size and each layer size, of length (number of layers + 1).
    learning_rate -- learning rate of the gradient descent update rule
    num_iterations -- number of iterations of the optimization loop
    print_cost -- if True, it prints the cost every 100 steps

    Returns:
    parameters -- parameters learnt by the model. They can then be used to predict.
    """
    np.random.seed(1)
    costs = []
    
    parameters = initialize_parameters_deep(layers_dims)

    for i in range(0,num_iterations):
        # Forward propagation: [LINEAR -> RELU]*(L-1) -> LINEAR -> SIGMOID.
        AL, caches = L_model_forward(X, parameters)
        # Compute cost.
        cost = compute_cost(AL, Y)

        # Backward propagation.
        grads = L_model_backward(AL, Y, caches)
        # Update parameters.
        parameters = update_parameters(parameters, grads, learning_rate)

        if print_cost and i % 100 == 0:
            print ("Cost after iteration %i: %f" %(i, cost))
        if print_cost and i % 100 == 0:
            costs.append(cost)

    return parameters
    
parameters = L_layer_model(train_x, train_y, layers_dims, num_iterations = 2500, print_cost = True)
pred_train = predict(train_x, train_y, parameters)
pred_test = predict(test_x, test_y, parameters)
