coding_nn_reg_load_lib.py

# Loading packages
import sys

import h5py
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns

sys.path.append("../scripts/")
from coding_neural_network_from_scratch import (initialize_parameters,
                                                L_model_forward,
                                                compute_cost,
                                                relu_gradient,
                                                sigmoid_gradient,
                                                tanh_gradient,
                                                update_parameters,
                                                accuracy)
from gradient_checking import dictionary_to_vector
from load_dataset import load_dataset_catvsdog

%matplotlib inline
sns.set_context("notebook")
plt.style.use("fivethirtyeight")
plt.rcParams['figure.figsize'] = (12, 6)

# Import training data
train_dataset = h5py.File("../data/train_catvnoncat.h5")
X_train = np.array(train_dataset["train_set_x"])
Y_train = np.array(train_dataset["train_set_y"])

# Plot a sample image
plt.imshow(X_train[50])
plt.axis("off");

# Import test data
test_dataset = h5py.File("../data/test_catvnoncat.h5")
X_test = np.array(test_dataset["test_set_x"])
Y_test = np.array(test_dataset["test_set_y"])

# Transform data
X_train = X_train.reshape(209, -1).T
X_train = X_train / 255
Y_train = Y_train.reshape(-1, 209)

X_test = X_test.reshape(50, -1).T
X_test = X_test / 255
Y_test = Y_test.reshape(-1, 50)

# print the new shape of both training and test datasets
print("Training data dimensions:")
print("X's dimension: {}, Y's dimension: {}".format(X_train.shape, Y_train.shape))
print("Test data dimensions:")
print("X's dimension: {}, Y's dimension: {}".format(X_test.shape, Y_test.shape))