from DeepNeuralNet4e import *

from learning4e import DataSet, grade_learner, err_ratio

from keras.datasets import imdb

import numpy as np

def test_neural_net():

iris = DataSet(name="iris")

classes = ["setosa", "versicolor", "virginica"]

iris.classes_to_numbers(classes)

nn_adam = neural_net_learner(iris, [4], learning_rate=0.001, epochs=200, optimizer=adam_optimizer)

nn_gd = neural_net_learner(iris, [4], learning_rate=0.15, epochs=100, optimizer=gradient_descent)

tests = [([5.0, 3.1, 0.9, 0.1], 0),

([5.1, 3.5, 1.0, 0.0], 0),

([4.9, 3.3, 1.1, 0.1], 0),

([6.0, 3.0, 4.0, 1.1], 1),

([6.1, 2.2, 3.5, 1.0], 1),

([5.9, 2.5, 3.3, 1.1], 1),

([7.5, 4.1, 6.2, 2.3], 2),

([7.3, 4.0, 6.1, 2.4], 2),

([7.0, 3.3, 6.1, 2.5], 2)]

assert grade_learner(nn_adam, tests) >= 1 / 3

assert grade_learner(nn_gd, tests) >= 1 / 3

assert err_ratio(nn_adam, iris) < 0.21

assert err_ratio(nn_gd, iris) < 0.21

def test_cross_entropy():

loss = cross_entropy_loss([1,0], [0.9, 0.3])

assert round(loss,2) == 0.23

loss = cross_entropy_loss([1,0,0,1], [0.9,0.3,0.5,0.75])

assert round(loss,2) == 0.36

loss = cross_entropy_loss([1,0,0,1,1,0,1,1], [0.9,0.3,0.5,0.75,0.85,0.14,0.93,0.79])

assert round(loss,2) == 0.26

def test_perceptron():

iris = DataSet(name="iris")

classes = ["setosa", "versicolor", "virginica"]

iris.classes_to_numbers(classes)

perceptron = perceptron_learner(iris, learning_rate=0.01, epochs=100)

tests = [([5, 3, 1, 0.1], 0),

([5, 3.5, 1, 0], 0),

([6, 3, 4, 1.1], 1),

([6, 2, 3.5, 1], 1),

([7.5, 4, 6, 2], 2),

([7, 3, 6, 2.5], 2)]

assert grade_learner(perceptron, tests) > 1/2

assert err_ratio(perceptron, iris) < 0.4

def test_rnn():

data = imdb.load_data(num_words=5000)

train, val, test = keras_dataset_loader(data)

train = (train[0][:1000], train[1][:1000])

val = (val[0][:200], val[1][:200])

model = simple_rnn_learner(train, val)

score = model.evaluate(test[0][:200], test[1][:200], verbose=0)

acc = score[1]

assert acc >= 0.3

def test_auto_encoder():

iris = DataSet(name="iris")

classes = ["setosa", "versicolor", "virginica"]

iris.classes_to_numbers(classes)

inputs = np.asarray(iris.examples)

# print(inputs[0])

model = auto_encoder_learner(inputs, 100)

print(inputs[0])

print(model.predict(inputs[:1]))