akirasosa/keras_prime_number.py

## keras_prime_number.py
import numpy as np
from keras.layers import Dense, Dropout, Activation
from keras.layers.advanced_activations import PReLU
from keras.models import Sequential
from matplotlib import pyplot as plt

seed = 7
np.random.seed(seed)

num_digits = 14  # binary encode numbers
max_number = 2 ** num_digits


def prime_list():
    counter = 0
    primes = [2, 3]

    for n in range(5, max_number, 2):
        is_prime = True
        for i in range(1, len(primes)):
            counter += 1
            if primes[i] ** 2 > n:
                break
            counter += 1
            if n % primes[i] == 0:
                is_prime = False
                break
        if is_prime:
            primes.append(n)
    return primes


primes = prime_list()


def prime_encode(i):
    if i in primes:
        return 1
    else:
        return 0


def bin_encode(i):
    return [i >> d & 1 for d in range(num_digits)]


def create_dataset():
    x, y = [], []
    for i in range(102, max_number):
        x.append(bin_encode(i))
        y.append(prime_encode(i))
    return np.array(x), y


x_train, y_train = create_dataset()

model = Sequential()
model.add(Dense(units=100, input_dim=num_digits))
model.add(PReLU())
model.add(Dropout(rate=0.2))
model.add(Dense(units=50))
model.add(PReLU())
model.add(Dropout(rate=0.2))
model.add(Dense(units=25))
model.add(PReLU())
model.add(Dropout(rate=0.2))
model.add(Dense(units=1))
model.add(Activation("sigmoid"))

model.compile(optimizer='RMSprop',
              loss='binary_crossentropy',
              metrics=['accuracy'])

history = model.fit(x_train, y_train, epochs=1000, batch_size=128,
                    validation_split=0.1)

# predict
errors, correct = 0, 0
tp, fn, fp = 0, 0, 0

for i in range(2, 101):
    x = bin_encode(i)
    y = model.predict(np.array(x).reshape(-1, num_digits))
    if y[0][0] >= 0.5:
        pred = 1
    else:
        pred = 0
    obs = prime_encode(i)
    print(i, obs, pred, y[0][0])
    if pred == obs:
        correct += 1
    else:
        errors += 1
    if obs == 1 and pred == 1:
        tp += 1
    if obs == 1 and pred == 0:
        fn += 1
    if obs == 0 and pred == 1:
        fp += 1

precision = tp / (tp + fp)
recall = tp / (tp + fn)
f_score = 2 * precision * recall / (precision + recall)

print("Errors :", errors, " Correct :", correct, "F score :", f_score)


def plot_history(history):
    plt.plot(history.history['loss'])
    plt.plot(history.history['val_loss'])
    plt.title('model loss')
    plt.xlabel('epoch')
    plt.ylabel('loss')
    plt.legend(['loss', 'val_loss'], loc='upper right')
    plt.savefig('RMSprop_more')


plot_history(history)
	import numpy as np
	from keras.layers import Dense, Dropout, Activation
	from keras.layers.advanced_activations import PReLU
	from keras.models import Sequential
	from matplotlib import pyplot as plt

	seed = 7
	np.random.seed(seed)

	num_digits = 14 # binary encode numbers
	max_number = 2 ** num_digits


	def prime_list():
	counter = 0
	primes = [2, 3]

	for n in range(5, max_number, 2):
	is_prime = True
	for i in range(1, len(primes)):
	counter += 1
	if primes[i] ** 2 > n:
	break
	counter += 1
	if n % primes[i] == 0:
	is_prime = False
	break
	if is_prime:
	primes.append(n)
	return primes


	primes = prime_list()


	def prime_encode(i):
	if i in primes:
	return 1
	else:
	return 0


	def bin_encode(i):
	return [i >> d & 1 for d in range(num_digits)]


	def create_dataset():
	x, y = [], []
	for i in range(102, max_number):
	x.append(bin_encode(i))
	y.append(prime_encode(i))
	return np.array(x), y


	x_train, y_train = create_dataset()

	model = Sequential()
	model.add(Dense(units=100, input_dim=num_digits))
	model.add(PReLU())
	model.add(Dropout(rate=0.2))
	model.add(Dense(units=50))
	model.add(PReLU())
	model.add(Dropout(rate=0.2))
	model.add(Dense(units=25))
	model.add(PReLU())
	model.add(Dropout(rate=0.2))
	model.add(Dense(units=1))
	model.add(Activation("sigmoid"))

	model.compile(optimizer='RMSprop',
	loss='binary_crossentropy',
	metrics=['accuracy'])

	history = model.fit(x_train, y_train, epochs=1000, batch_size=128,
	validation_split=0.1)

	# predict
	errors, correct = 0, 0
	tp, fn, fp = 0, 0, 0

	for i in range(2, 101):
	x = bin_encode(i)
	y = model.predict(np.array(x).reshape(-1, num_digits))
	if y[0][0] >= 0.5:
	pred = 1
	else:
	pred = 0
	obs = prime_encode(i)
	print(i, obs, pred, y[0][0])
	if pred == obs:
	correct += 1
	else:
	errors += 1
	if obs == 1 and pred == 1:
	tp += 1
	if obs == 1 and pred == 0:
	fn += 1
	if obs == 0 and pred == 1:
	fp += 1

	precision = tp / (tp + fp)
	recall = tp / (tp + fn)
	f_score = 2 * precision * recall / (precision + recall)

	print("Errors :", errors, " Correct :", correct, "F score :", f_score)


	def plot_history(history):
	plt.plot(history.history['loss'])
	plt.plot(history.history['val_loss'])
	plt.title('model loss')
	plt.xlabel('epoch')
	plt.ylabel('loss')
	plt.legend(['loss', 'val_loss'], loc='upper right')
	plt.savefig('RMSprop_more')


	plot_history(history)