已经标注好数据集了，怎么实现卷积深度网络训练分类python
import tensorflow as tf
import pathlib
imgdir_path = pathlib.Path('D:\\1')
file_list = sorted([str(path) for path in imgdir_path.glob('*.jpg')])
print(file_list)
import matplotlib.pyplot as plt
import os
fig = plt.figure(figsize=(10, 5))
for i,file in enumerate(file_list):
img_raw = tf.io.read_file(file)
img = tf.image.decode_image(img_raw)
print('Image shape: ', img.shape)
ax = fig.add_subplot(2, 800, i+1)
ax.set_xticks([]); ax.set_yticks([])
ax.imshow(img)
ax.set_title(os.path.basename(file), size=15)
# plt.savefig('ch13-catdot-examples.pdf')
plt.tight_layout()
plt.show()
labels = [1 if 'malignant' in os.path.basename(file) else 0
for file in file_list]
print(labels)
ds_files_labels = tf.data.Dataset.from_tensor_slices(
(file_list, labels))
for item in ds_files_labels:
print(item[0].numpy(), item[1].numpy())
def load_and_preprocess(path, label):
image = tf.io.read_file(path)
image = tf.image.decode_jpeg(image, channels=3)
image = tf.image.resize(image, [img_height, img_width])
image /= 255.0
return image, label
img_width, img_height = 120, 80
ds_images_labels = ds_files_labels.map(load_and_preprocess)
fig = plt.figure(figsize=(10, 5))
for i,example in enumerate(ds_images_labels):
print(example[0].shape, example[1].numpy())
ax = fig.add_subplot(2, 800, i+1)
ax.set_xticks([]); ax.set_yticks([])
ax.imshow(example[0])
ax.set_title('{}'.format(example[1].numpy()),
size=15)
plt.tight_layout()
#plt.savefig('ch13-catdog-dataset.pdf')
plt.show()
