Realice el mapeo de la imagen al estilo artístico.
Esto se ejecutó en una computadora portátil delgada y liviana hace mucho tiempo, la tarjeta gráfica es MX250,
memoria de video 2G. Hice un pequeño conjunto de datos de cientos de imágenes en el PPT de historia del arte occidental y me entrené con este conjunto de datos.
from data_utils.data_utils import load_art5
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
from model.squeezenet import SqueezeNet
from tensorflow.keras.applications.resnet50 import ResNet50
from model.backprop_squeezenet import SqueezeNet as backprop_SqueezeNet
from pathlib import Path
import os
import datetime
%load_ext tensorboard
x_train, y_train, x_test, y_test, label_to_name = load_art5()
print('x_train',x_train.shape)
print('y_train',y_train.shape)
print('x_test', x_test.shape)
print('y_test', y_test.shape)
print("\n标签对应的风格：\n",label_to_name)
x_train (250, 224, 224, 3)
y_train (250,)
x_test (40, 224, 224, 3)
y_test (40,)
标签对应的风格：
{0: '古埃及', 1: '印象派', 2: '立体派', 3: '纯粹主义', 4: '灯光效应艺术、奥普艺术'}
learning_rate = [2e-4]
model = SqueezeNet(num_classes = 5)
def lr_schedule(epoch):
# 学习速率的变化表
learning_rate = 1e-2
if epoch > 100:
learning_rate = 1e-3
if epoch > 200:
learning_rate = 1e-5
if epoch > 1000:
learning_rate = 2e-7
if epoch > 1500:
learning_rate = 1e-9
tf.summary.scalar('learning rate', data=learning_rate, step=epoch)
return learning_rate
#tune learning rate
for le in learning_rate:
print('learning_rate', le)
print('---------------------------------------------------------------------------------')
log_dir="logs/train1/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
file_writer = tf.summary.create_file_writer(log_dir + "/metrics")
file_writer.set_as_default()
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=Path(log_dir), histogram_freq=1)
lr_callback = tf.keras.callbacks.LearningRateScheduler(lr_schedule, verbose=1)
optimizer = tf.keras.optimizers.Adam(le)
#model.load_weights('./model/squeezenet_weight')
model.compile(optimizer= optimizer,loss='sparse_categorical_crossentropy',
metrics=[tf.keras.metrics.sparse_categorical_accuracy])
model.fit(x_train, y_train, epochs=100,batch_size=50, validation_data = (x_test, y_test), verbose = 2,
callbacks=[tensorboard_callback])
model.evaluate(x_test, y_test, verbose = 1)
model.save_weights('./model/trained_model/art5')
learning_rate 0.0002
---------------------------------------------------------------------------------
Epoch 1/100
WARNING:tensorflow:From C:\Users\Yishif\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\ops\summary_ops_v2.py:1277: stop (from tensorflow.python.eager.profiler) is deprecated and will be removed after 2020-07-01.
Instructions for updating:
use `tf.profiler.experimental.stop` instead.
WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0319s vs `on_train_batch_end` time: 0.4966s). Check your callbacks.
5/5 - 3s - loss: 2.9243 - sparse_categorical_accuracy: 0.1600 - val_loss: 2.8109 - val_sparse_categorical_accuracy: 0.1000
Epoch 2/100
5/5 - 2s - loss: 2.4745 - sparse_categorical_accuracy: 0.1840 - val_loss: 2.3348 - val_sparse_categorical_accuracy: 0.3500
Epoch 3/100
5/5 - 2s - loss: 2.1843 - sparse_categorical_accuracy: 0.3840 - val_loss: 2.1565 - val_sparse_categorical_accuracy: 0.3750
Epoch 4/100
5/5 - 2s - loss: 2.0166 - sparse_categorical_accuracy: 0.3560 - val_loss: 2.0870 - val_sparse_categorical_accuracy: 0.3500
Epoch 5/100
5/5 - 2s - loss: 1.8970 - sparse_categorical_accuracy: 0.4160 - val_loss: 2.0318 - val_sparse_categorical_accuracy: 0.3750
Epoch 6/100
5/5 - 2s - loss: 1.8280 - sparse_categorical_accuracy: 0.3720 - val_loss: 1.9484 - val_sparse_categorical_accuracy: 0.4000
Epoch 7/100
5/5 - 2s - loss: 1.7702 - sparse_categorical_accuracy: 0.4120 - val_loss: 1.9097 - val_sparse_categorical_accuracy: 0.3750
Epoch 8/100
5/5 - 2s - loss: 1.7212 - sparse_categorical_accuracy: 0.4160 - val_loss: 1.8980 - val_sparse_categorical_accuracy: 0.4000
Epoch 9/100
5/5 - 2s - loss: 1.6827 - sparse_categorical_accuracy: 0.4080 - val_loss: 1.8328 - val_sparse_categorical_accuracy: 0.4000
Epoch 10/100
5/5 - 2s - loss: 1.6674 - sparse_categorical_accuracy: 0.4560 - val_loss: 1.8411 - val_sparse_categorical_accuracy: 0.4000
Epoch 11/100
5/5 - 2s - loss: 1.6331 - sparse_categorical_accuracy: 0.4200 - val_loss: 1.8294 - val_sparse_categorical_accuracy: 0.3750
Epoch 12/100
5/5 - 2s - loss: 1.6046 - sparse_categorical_accuracy: 0.4480 - val_loss: 1.8084 - val_sparse_categorical_accuracy: 0.3750
Epoch 13/100
5/5 - 2s - loss: 1.5849 - sparse_categorical_accuracy: 0.4640 - val_loss: 1.7869 - val_sparse_categorical_accuracy: 0.3750
Epoch 14/100
5/5 - 2s - loss: 1.5783 - sparse_categorical_accuracy: 0.4520 - val_loss: 1.7202 - val_sparse_categorical_accuracy: 0.3750
Epoch 15/100
5/5 - 2s - loss: 1.5890 - sparse_categorical_accuracy: 0.4480 - val_loss: 1.7938 - val_sparse_categorical_accuracy: 0.3750
Epoch 16/100
5/5 - 2s - loss: 1.5535 - sparse_categorical_accuracy: 0.5000 - val_loss: 1.7424 - val_sparse_categorical_accuracy: 0.3750
Epoch 17/100
5/5 - 2s - loss: 1.5241 - sparse_categorical_accuracy: 0.4760 - val_loss: 1.7203 - val_sparse_categorical_accuracy: 0.3750
Epoch 18/100
5/5 - 2s - loss: 1.5358 - sparse_categorical_accuracy: 0.4640 - val_loss: 1.6901 - val_sparse_categorical_accuracy: 0.4000
Epoch 19/100
5/5 - 2s - loss: 1.5553 - sparse_categorical_accuracy: 0.4400 - val_loss: 1.8723 - val_sparse_categorical_accuracy: 0.3750
Epoch 20/100
5/5 - 2s - loss: 1.5542 - sparse_categorical_accuracy: 0.4840 - val_loss: 1.7149 - val_sparse_categorical_accuracy: 0.4250
Epoch 21/100
5/5 - 2s - loss: 1.5115 - sparse_categorical_accuracy: 0.4960 - val_loss: 1.8497 - val_sparse_categorical_accuracy: 0.4000
Epoch 22/100
5/5 - 2s - loss: 1.4885 - sparse_categorical_accuracy: 0.4760 - val_loss: 1.7004 - val_sparse_categorical_accuracy: 0.3750
Epoch 23/100
5/5 - 2s - loss: 1.4982 - sparse_categorical_accuracy: 0.4560 - val_loss: 1.8699 - val_sparse_categorical_accuracy: 0.4000
Epoch 24/100
5/5 - 2s - loss: 1.4815 - sparse_categorical_accuracy: 0.4520 - val_loss: 1.6570 - val_sparse_categorical_accuracy: 0.2750
Epoch 25/100
5/5 - 2s - loss: 1.4591 - sparse_categorical_accuracy: 0.4600 - val_loss: 1.8539 - val_sparse_categorical_accuracy: 0.4000
Epoch 26/100
5/5 - 2s - loss: 1.4765 - sparse_categorical_accuracy: 0.5080 - val_loss: 1.6151 - val_sparse_categorical_accuracy: 0.3500
Epoch 27/100
5/5 - 2s - loss: 1.4180 - sparse_categorical_accuracy: 0.4960 - val_loss: 1.6629 - val_sparse_categorical_accuracy: 0.3750
Epoch 28/100
5/5 - 2s - loss: 1.4058 - sparse_categorical_accuracy: 0.5400 - val_loss: 1.7261 - val_sparse_categorical_accuracy: 0.3500
Epoch 29/100
5/5 - 2s - loss: 1.3546 - sparse_categorical_accuracy: 0.5280 - val_loss: 1.6914 - val_sparse_categorical_accuracy: 0.3500
Epoch 30/100
5/5 - 2s - loss: 1.3454 - sparse_categorical_accuracy: 0.5320 - val_loss: 1.7979 - val_sparse_categorical_accuracy: 0.3500
Epoch 31/100
5/5 - 2s - loss: 1.3643 - sparse_categorical_accuracy: 0.5440 - val_loss: 1.6637 - val_sparse_categorical_accuracy: 0.4250
Epoch 32/100
5/5 - 2s - loss: 1.3620 - sparse_categorical_accuracy: 0.5800 - val_loss: 1.5831 - val_sparse_categorical_accuracy: 0.4250
Epoch 33/100
5/5 - 2s - loss: 1.3816 - sparse_categorical_accuracy: 0.5480 - val_loss: 1.7300 - val_sparse_categorical_accuracy: 0.4250
Epoch 34/100
5/5 - 2s - loss: 1.3421 - sparse_categorical_accuracy: 0.5920 - val_loss: 1.6342 - val_sparse_categorical_accuracy: 0.3750
Epoch 35/100
5/5 - 2s - loss: 1.2748 - sparse_categorical_accuracy: 0.5960 - val_loss: 1.6406 - val_sparse_categorical_accuracy: 0.4750
Epoch 36/100
5/5 - 2s - loss: 1.2682 - sparse_categorical_accuracy: 0.5680 - val_loss: 1.7089 - val_sparse_categorical_accuracy: 0.4000
Epoch 37/100
5/5 - 2s - loss: 1.1762 - sparse_categorical_accuracy: 0.6240 - val_loss: 1.6893 - val_sparse_categorical_accuracy: 0.5000
Epoch 38/100
5/5 - 2s - loss: 1.1809 - sparse_categorical_accuracy: 0.6440 - val_loss: 1.5903 - val_sparse_categorical_accuracy: 0.4000
Epoch 39/100
5/5 - 2s - loss: 1.2160 - sparse_categorical_accuracy: 0.6480 - val_loss: 1.8914 - val_sparse_categorical_accuracy: 0.4000
Epoch 40/100
5/5 - 2s - loss: 1.2396 - sparse_categorical_accuracy: 0.6160 - val_loss: 1.6752 - val_sparse_categorical_accuracy: 0.4250
Epoch 41/100
5/5 - 2s - loss: 1.2391 - sparse_categorical_accuracy: 0.6680 - val_loss: 1.7163 - val_sparse_categorical_accuracy: 0.4500
Epoch 42/100
5/5 - 2s - loss: 1.1911 - sparse_categorical_accuracy: 0.6600 - val_loss: 1.6151 - val_sparse_categorical_accuracy: 0.4250
Epoch 43/100
5/5 - 2s - loss: 1.1188 - sparse_categorical_accuracy: 0.6840 - val_loss: 1.7541 - val_sparse_categorical_accuracy: 0.4750
Epoch 44/100
5/5 - 2s - loss: 1.0513 - sparse_categorical_accuracy: 0.6840 - val_loss: 1.6569 - val_sparse_categorical_accuracy: 0.5000
Epoch 45/100
5/5 - 2s - loss: 0.9712 - sparse_categorical_accuracy: 0.7440 - val_loss: 1.8007 - val_sparse_categorical_accuracy: 0.5250
Epoch 46/100
5/5 - 2s - loss: 0.9272 - sparse_categorical_accuracy: 0.7440 - val_loss: 2.0097 - val_sparse_categorical_accuracy: 0.5500
Epoch 47/100
5/5 - 2s - loss: 0.8684 - sparse_categorical_accuracy: 0.7880 - val_loss: 2.0083 - val_sparse_categorical_accuracy: 0.5250
Epoch 48/100
5/5 - 2s - loss: 0.8466 - sparse_categorical_accuracy: 0.7560 - val_loss: 1.9569 - val_sparse_categorical_accuracy: 0.5500
Epoch 49/100
5/5 - 2s - loss: 0.8995 - sparse_categorical_accuracy: 0.7760 - val_loss: 2.0226 - val_sparse_categorical_accuracy: 0.5000
Epoch 50/100
5/5 - 2s - loss: 0.9518 - sparse_categorical_accuracy: 0.7400 - val_loss: 1.9933 - val_sparse_categorical_accuracy: 0.4750
Epoch 51/100
5/5 - 2s - loss: 0.8644 - sparse_categorical_accuracy: 0.7920 - val_loss: 2.3111 - val_sparse_categorical_accuracy: 0.4500
Epoch 52/100
5/5 - 2s - loss: 0.8003 - sparse_categorical_accuracy: 0.8200 - val_loss: 2.5071 - val_sparse_categorical_accuracy: 0.4500
Epoch 53/100
5/5 - 2s - loss: 0.7072 - sparse_categorical_accuracy: 0.8760 - val_loss: 2.4365 - val_sparse_categorical_accuracy: 0.5000
Epoch 54/100
5/5 - 2s - loss: 0.6829 - sparse_categorical_accuracy: 0.8640 - val_loss: 2.8239 - val_sparse_categorical_accuracy: 0.4750
Epoch 55/100
5/5 - 2s - loss: 0.7343 - sparse_categorical_accuracy: 0.8480 - val_loss: 2.5380 - val_sparse_categorical_accuracy: 0.5000
Epoch 56/100
5/5 - 2s - loss: 0.6952 - sparse_categorical_accuracy: 0.8520 - val_loss: 2.7319 - val_sparse_categorical_accuracy: 0.4500
Epoch 57/100
5/5 - 2s - loss: 0.6705 - sparse_categorical_accuracy: 0.8840 - val_loss: 2.6535 - val_sparse_categorical_accuracy: 0.5000
Epoch 58/100
5/5 - 2s - loss: 0.6237 - sparse_categorical_accuracy: 0.9040 - val_loss: 2.7103 - val_sparse_categorical_accuracy: 0.5250
Epoch 59/100
5/5 - 2s - loss: 0.6020 - sparse_categorical_accuracy: 0.8960 - val_loss: 2.8843 - val_sparse_categorical_accuracy: 0.5000
Epoch 60/100
5/5 - 2s - loss: 0.5429 - sparse_categorical_accuracy: 0.9200 - val_loss: 2.7795 - val_sparse_categorical_accuracy: 0.5250
Epoch 61/100
5/5 - 2s - loss: 0.5071 - sparse_categorical_accuracy: 0.9520 - val_loss: 2.9273 - val_sparse_categorical_accuracy: 0.5750
Epoch 62/100
5/5 - 2s - loss: 0.4594 - sparse_categorical_accuracy: 0.9600 - val_loss: 3.2589 - val_sparse_categorical_accuracy: 0.5250
Epoch 63/100
5/5 - 2s - loss: 0.4720 - sparse_categorical_accuracy: 0.9600 - val_loss: 3.6066 - val_sparse_categorical_accuracy: 0.5750
Epoch 64/100
5/5 - 2s - loss: 0.4593 - sparse_categorical_accuracy: 0.9360 - val_loss: 3.8621 - val_sparse_categorical_accuracy: 0.5000
Epoch 65/100
5/5 - 2s - loss: 0.4724 - sparse_categorical_accuracy: 0.9400 - val_loss: 4.0097 - val_sparse_categorical_accuracy: 0.5500
Epoch 66/100
5/5 - 2s - loss: 0.4126 - sparse_categorical_accuracy: 0.9640 - val_loss: 3.4587 - val_sparse_categorical_accuracy: 0.5250
Epoch 67/100
5/5 - 2s - loss: 0.4057 - sparse_categorical_accuracy: 0.9840 - val_loss: 4.7477 - val_sparse_categorical_accuracy: 0.5250
Epoch 68/100
5/5 - 2s - loss: 0.4589 - sparse_categorical_accuracy: 0.9440 - val_loss: 3.6056 - val_sparse_categorical_accuracy: 0.5750
Epoch 69/100
5/5 - 2s - loss: 0.5638 - sparse_categorical_accuracy: 0.9160 - val_loss: 4.1461 - val_sparse_categorical_accuracy: 0.5250
Epoch 70/100
5/5 - 2s - loss: 1.0732 - sparse_categorical_accuracy: 0.7840 - val_loss: 3.0761 - val_sparse_categorical_accuracy: 0.4750
Epoch 71/100
5/5 - 2s - loss: 0.7008 - sparse_categorical_accuracy: 0.8560 - val_loss: 2.8879 - val_sparse_categorical_accuracy: 0.4000
Epoch 72/100
5/5 - 2s - loss: 0.7639 - sparse_categorical_accuracy: 0.8600 - val_loss: 1.9902 - val_sparse_categorical_accuracy: 0.5750
Epoch 73/100
5/5 - 2s - loss: 0.6854 - sparse_categorical_accuracy: 0.9000 - val_loss: 2.7760 - val_sparse_categorical_accuracy: 0.6250
Epoch 74/100
5/5 - 2s - loss: 0.5475 - sparse_categorical_accuracy: 0.9520 - val_loss: 3.0067 - val_sparse_categorical_accuracy: 0.6000
Epoch 75/100
5/5 - 2s - loss: 0.5402 - sparse_categorical_accuracy: 0.9520 - val_loss: 3.2242 - val_sparse_categorical_accuracy: 0.5500
Epoch 76/100
5/5 - 2s - loss: 0.4982 - sparse_categorical_accuracy: 0.9560 - val_loss: 3.4900 - val_sparse_categorical_accuracy: 0.5750
Epoch 77/100
5/5 - 2s - loss: 0.4636 - sparse_categorical_accuracy: 0.9800 - val_loss: 3.5054 - val_sparse_categorical_accuracy: 0.6000
Epoch 78/100
5/5 - 2s - loss: 0.4421 - sparse_categorical_accuracy: 0.9800 - val_loss: 3.8175 - val_sparse_categorical_accuracy: 0.5500
Epoch 79/100
5/5 - 2s - loss: 0.4156 - sparse_categorical_accuracy: 0.9800 - val_loss: 4.0961 - val_sparse_categorical_accuracy: 0.5500
Epoch 80/100
5/5 - 2s - loss: 0.3983 - sparse_categorical_accuracy: 0.9840 - val_loss: 4.0084 - val_sparse_categorical_accuracy: 0.5000
Epoch 81/100
5/5 - 2s - loss: 0.3933 - sparse_categorical_accuracy: 0.9880 - val_loss: 4.3952 - val_sparse_categorical_accuracy: 0.5500
Epoch 82/100
5/5 - 2s - loss: 0.3802 - sparse_categorical_accuracy: 0.9880 - val_loss: 4.5125 - val_sparse_categorical_accuracy: 0.5750
Epoch 83/100
5/5 - 2s - loss: 0.3598 - sparse_categorical_accuracy: 0.9920 - val_loss: 4.6252 - val_sparse_categorical_accuracy: 0.5250
Epoch 84/100
5/5 - 2s - loss: 0.3486 - sparse_categorical_accuracy: 0.9960 - val_loss: 4.8283 - val_sparse_categorical_accuracy: 0.5250
Epoch 85/100
5/5 - 2s - loss: 0.3814 - sparse_categorical_accuracy: 0.9840 - val_loss: 4.2472 - val_sparse_categorical_accuracy: 0.5750
Epoch 86/100
5/5 - 2s - loss: 0.3722 - sparse_categorical_accuracy: 0.9840 - val_loss: 4.4263 - val_sparse_categorical_accuracy: 0.5500
Epoch 87/100
5/5 - 2s - loss: 0.3801 - sparse_categorical_accuracy: 0.9720 - val_loss: 4.6447 - val_sparse_categorical_accuracy: 0.5750
Epoch 88/100
5/5 - 2s - loss: 0.3513 - sparse_categorical_accuracy: 0.9920 - val_loss: 4.6057 - val_sparse_categorical_accuracy: 0.5750
Epoch 89/100
5/5 - 2s - loss: 0.3604 - sparse_categorical_accuracy: 0.9880 - val_loss: 4.5952 - val_sparse_categorical_accuracy: 0.5250
Epoch 90/100
5/5 - 2s - loss: 0.4851 - sparse_categorical_accuracy: 0.9600 - val_loss: 4.0727 - val_sparse_categorical_accuracy: 0.5750
Epoch 91/100
5/5 - 2s - loss: 0.3744 - sparse_categorical_accuracy: 0.9800 - val_loss: 3.9599 - val_sparse_categorical_accuracy: 0.5750
Epoch 92/100
5/5 - 2s - loss: 0.3490 - sparse_categorical_accuracy: 1.0000 - val_loss: 4.1361 - val_sparse_categorical_accuracy: 0.6250
Epoch 93/100
5/5 - 2s - loss: 0.3401 - sparse_categorical_accuracy: 0.9960 - val_loss: 4.3469 - val_sparse_categorical_accuracy: 0.6000
Epoch 94/100
5/5 - 2s - loss: 0.3290 - sparse_categorical_accuracy: 0.9960 - val_loss: 4.9883 - val_sparse_categorical_accuracy: 0.6250
Epoch 95/100
5/5 - 2s - loss: 0.3250 - sparse_categorical_accuracy: 0.9960 - val_loss: 5.0136 - val_sparse_categorical_accuracy: 0.6000
Epoch 96/100
5/5 - 2s - loss: 0.3181 - sparse_categorical_accuracy: 1.0000 - val_loss: 4.8905 - val_sparse_categorical_accuracy: 0.6000
Epoch 97/100
5/5 - 2s - loss: 0.3051 - sparse_categorical_accuracy: 1.0000 - val_loss: 5.0910 - val_sparse_categorical_accuracy: 0.6000
Epoch 98/100
5/5 - 2s - loss: 0.3001 - sparse_categorical_accuracy: 1.0000 - val_loss: 5.3141 - val_sparse_categorical_accuracy: 0.6000
Epoch 99/100
5/5 - 2s - loss: 0.2903 - sparse_categorical_accuracy: 1.0000 - val_loss: 5.3422 - val_sparse_categorical_accuracy: 0.6000
Epoch 100/100
5/5 - 2s - loss: 0.2850 - sparse_categorical_accuracy: 1.0000 - val_loss: 5.2941 - val_sparse_categorical_accuracy: 0.6000
2/2 [==============================] - 1s 341ms/step - loss: 5.2941 - sparse_categorical_accuracy: 0.6000
%tensorboard --logdir logs/train1 --port=6008
Reusing TensorBoard on port 6008 (pid 20356), started 0:00:08 ago. (Use '!kill 20356' to kill it.)
#得到测试输出
y = model.predict(x_test)
y = np.argmax(y, axis = 1)
Como puede verse en el proceso de entrenamiento de la figura anterior, la tasa de precisión del conjunto de entrenamiento puede alcanzar el 100 %. Solo porque el conjunto de entrenamiento es relativamente pequeño, la tasa de precisión del conjunto de prueba solo alcanzó el 60% y apareció el fenómeno de ajuste excesivo, pero esto es suficiente para mostrar que la red neuronal convolucional no se limita al reconocimiento de características, es esencialmente una función de ajuste, y CNN todavía es muy capaz de ajustar cosas relacionadas con las características. Por ejemplo, para la clasificación de estilos, las redes neuronales convolucionales pueden lograr este tipo de ajuste.
A continuación, comience a probar los resultados del entrenamiento.
Se puede ver que la tasa de precisión supera el 50 %, si la muestra de datos de entrenamiento es lo suficientemente grande, teóricamente puede alcanzar la misma tasa de precisión del 100 % que el conjunto de entrenamiento.
plt.figure(dpi=100)
for i in range(3*3):
plt.subplot(3,3,i+1)
i = i+6
title = "label:"+str(int(y_test[i]))+"predict:"+str(y[i])
plt.title(title)
plt.imshow(x_test[i])
plt.axis('off')
print("标签与类别的对应关系：",label_to_name)
标签与类别的对应关系： {0: '古埃及', 1: '印象派', 2: '立体派', 3: '纯粹主义', 4: '灯光效应艺术、奥普艺术'}
