영상입력 수치 예측 모델 레시피(다층퍼셉트론 신경망 모델)

# 1. 사용할 패키지 불러오기

import numpy as np
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense
%matplotlib inline

# 2. 데이터 생성하기
width  = 16
height = 16

def generate_dataset(samples):
        ds_x = []
        ds_y = []

for it in range(samples):
       num_pt = np.random.randint(0, width * height)
       img = generate_image(num_pt)

              ds_y.append(num_pt)
              ds_x.append(img)

return np.array(ds_x), np.array(ds_y).reshape(samples, 1)

def generate_image(points):
       img = np.zeros((width, height))
       pts = np.random.random((points, 2))

for ipt in pts:
   img[int(ipt[0] * width), int(ipt[1] * height)] = 1

return img.reshape(width, height, 1)

x_train, y_train = generate_dataset(1500)
x_val, y_val = generate_dataset(300)
x_test, y_test = generate_dataset(100)

x_train_1d = x_train.reshape(x_train.shape[0], width * height)
x_val_1d = x_val.reshape(x_val.shape[0], width * height)
x_test_1d = x_test.reshape(x_test.shape[0], width * height)

# 3. 모델 구성하기
model = Sequential()
model.add(Dense(input_dim = width * height, output_dim = 256, activation = "relu"))
model.add(Dense(input_dim = 256, output_dim = 256, activation = "relu"))
model.add(Dense(input_dim = 256, output_dim = 256))
model.add(Dense(input_dim = 256, output_dim = 1))

# 4. 모델 학습과정 설정하기
model.compile(loss = "mse", optimizer = "adam")

# 5. 모델 학습시키기
hist = model.fit(x_train_1d, y_train, batch_size = 32, epochs = 1000, validation_data = (x_val_1d, y_val))

# 6. 학습과정 살펴보기
plt.plot(hist.history["loss"])
plt.plot(hist.history["val_loss"])
plt.ylim(0.0, 300.0)
plt.ylabel("loss")
plt.xlabel("epoch")
plt.legend(["train", "val"], loc = "upper left")
plt.show()

# 7. 모델 평가하기
score = model.evaluate(x_test_1d, y_test, batch_size = 32)
print(score)

# 8. 모델 사용하기
yhat_test = model.predict(x_test_1d, batch_size = 32)

plt_row = 5
plt_col = 5

plt.rcParams["figure.figsize"] = (10,10)

f, axarr = plt.subplots(plt_row, plt_col)

for i in range(plt_row*plt_col):
    sub_plt = axarr[int(i/plt_row), int(i%plt_col)]
    sub_plt.axis('off')
    sub_plt.imshow(x_test[i].reshape(width, height))
    sub_plt.set_title('R %d P %.1f' % (y_test[i][0], yhat_test[i][0]))

plt.show()

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