def test():
network = SimpleConvNet(input_dim=(3,128,128),
conv_param = {'filter_num': [32,32], 'filter_size': [3,3], 'pad': [1,1], 'stride': [1,1]},
hidden_size=100, output_size=12, weight_init_std=0.01)
network.load_params("params.pkl")
data_transform = transforms.Compose([
transforms.RandomResizedCrop(128),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
path = join('dataset\plant-seedlings-classification', 'test')
sample_submission = pd.read_csv('sample_submission.csv')
submission = sample_submission.copy()
for i, f in enumerate(sample_submission['file']):
print(join(path, f))
image = Image.open(join(path, f))
#convert image
if image.mode != "RGB":
image = image.convert("RGB")
image = data_transform(image)
image = np.expand_dims(image, axis=0)
output = network.predict(image)
output = np.argmax(output, axis=1)
submission['species'][i] = one_hot_key[output[0]]
submission.to_csv('submission.csv', index=False)
img28 = np.array(img.resize((28,28)))
img.save('img.jpg')
pil_img_gray = Image.fromarray(np.uint8(img28)).save('img28.jpg')
thresh = np.median(img28)
if thresh > 127:
img28 = 255 - img28
x = np.zeros(img28.shape, img28.dtype)
x[np.where(img28 > thresh)] = 255
pil_img_gray = Image.fromarray(np.uint8(x)).save('result.jpg')
x = np.ravel(x)/255
# 入力値をテキストで出力
for i in range(0, 28):
for j in range(0, 28):
print("{:01.0f}".format(x[28*i+j]), end=' ')
print('')
# 畳み込みニューラルネットワークで推測させる
x = x.reshape((1, 1, 28, 28))
y = network.predict(x)
y = softmax(y.reshape(10))
# 推測結果を出力
y_idx = np.argsort(y)
for i in range(10):
print(y_idx[9-i], ":", round( y[y_idx[9-i]]*100, 2), "%" )
# データの読み込み
(x_train, t_train), (x_test, t_test) = load_cifar10(normalize=False, flatten=False)
#x_train = x_train[:1000]
#t_train = t_train[:1000]
x_train = x_train * 2.0 - 255
x_test = x_test * 2.0 - 255
batch_size=100
network = SimpleConvNet(input_dim=(3,32,32),
conv_param = {'filter_num': (32, 32, 64), 'filter_size': 3, 'pad': 1, 'stride': 1},
hidden_size=512, output_size=10, weight_init_std=0.01)
# パラメータの復帰
network.load_params("params.pkl")
print("Loaded Network Parameters!")
tt_array=np.empty((0,10),np.float32)
for i in range(int(x_train.shape[0] / batch_size)):
tx = x_train[i*batch_size:(i+1)*batch_size]
tt = network.predict(tx, train_flg=False)
tt = softmax(tt).get()
tt_array=np.concatenate((tt_array,tt),axis=0)
#tt_array=tt_array.reshape(-1,10)
with open("ttarray.pkl", 'wb') as f:
pickle.dump(tt_array, f)
