Main.py

import time
import numpy as np
import h5py
import matplotlib.pyplot as plt
from PIL import Image
plt.rcParams['figure.figsize'] = (5.0, 4.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'


# m_train = train_x_orig.shape[0]
# num_px = train_x_orig.shape[1]
# m_test = test_x_orig.shape[0]

np.random.seed(1)

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


def L_layer_model(X, Y, layers_dims, learning_rate = 0.0075, num_iterations = 3000, print_cost=False):#lr was 0.009

    np.random.seed(1)
    costs = []                         # keep track of cost

    parameters = None

    for i in range(0, num_iterations):

        cost = None

        grads = None

        parameters = None
        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)

    # plot the cost
    plt.plot(np.squeeze(costs))
    plt.ylabel('cost')
    plt.xlabel('iterations (per tens)')
    plt.title("Learning rate =" + str(learning_rate))
    plt.show()

    return parameters