coding_nn_load_data.py

# Import training dataset
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"])

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"])

# print the shape of input data and label vector
print(f"""Original dimensions:\n{20 * '-'}\nTraining: {X_train.shape}, {y_train.shape}
Test: {X_test.shape}, {y_test.shape}""")

# plot cat image
plt.figure(figsize=(6, 6))
plt.imshow(X_train[50])
plt.axis("off");

# Transform input data and label vector
X_train = X_train.reshape(209, -1).T
y_train = y_train.reshape(-1, 209)

X_test = X_test.reshape(50, -1).T
y_test = y_test.reshape(-1, 50)

# standardize the data
X_train = X_train / 255
X_test = X_test / 255

print(f"""\nNew dimensions:\n{15 * '-'}\nTraining: {X_train.shape}, {y_train.shape}
Test: {X_test.shape}, {y_test.shape}""")