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train1.py
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train1.py
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import sys
import os
import numpy as np
import cv2
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow import keras
numbers = ['0', '2', '3', '4', '5', '6', '7', '8', '9', '10']
alphbets = ['A', 'J', 'Q', 'K']
shape = ['hongtao', 'fangpian', 'heitao', 'meihua']
other = ['other']
dataset = numbers + alphbets + shape + other
dataset_len = len(dataset)
num_epochs = 50
batch_size = 100
learning_rate = 0.001
cur_dir = sys.path[0]
train_data_dir = os.path.join(cur_dir, 'pic/train')
train_model_path = os.path.join(cur_dir, 'model/char_recongnize/model1.ckpt')
test_data_dir = os.path.join(cur_dir, 'pic/test')
def list_all_files(root):
files = []
list = os.listdir(root)
for i in range(len(list)):
element = os.path.join(root, list[i])
if list[i] == '.DS_Store':
continue
if os.path.isdir(element):
temp_dir = os.path.split(element)[-1]
if temp_dir in dataset:
files.extend(list_all_files(element))
elif os.path.isfile(element):
files.append(element)
return files
def init_data(dir):
X = []
y = []
if not os.path.exists(train_data_dir):
raise ValueError('没有找到文件夹')
files = list_all_files(dir)
for file in files:
src_img = cv2.imread(file, cv2.COLOR_BGR2GRAY)
if src_img.ndim == 3:
continue
resize_img = cv2.resize(src_img, (20, 20))
X.append(resize_img)
# 获取图片文件全目录
dir = os.path.dirname(file)
# 获取图片文件上一级目录名
dir_name = os.path.split(dir)[-1]
# vector_y = [0 for i in range(len(dataset))]
index_y = dataset.index(dir_name)
# vector_y[index_y] = 1
y.append(index_y)
X = np.array(X)
X = X.reshape(-1,20,20,1)
y = np.array(y)
return X, y
def init_testData(dir):
test_X = []
if not os.path.exists(dir):
raise ValueError('没有找到文件夹')
files = list_all_files(dir)
for file in files:
src_img = cv2.imread(file, cv2.COLOR_BGR2GRAY)
if src_img.ndim == 3:
continue
resize_img = cv2.resize(src_img, (20, 20))
test_X.append(resize_img)
test_X = np.array(test_X)
test_X = test_X.reshape(-1, 20, 20, 1)
test_X = test_X.astype('float32')
return test_X
def train():
# 加载训练集
X, y = init_data(train_data_dir)
train_dataset = tf.data.Dataset.from_tensor_slices((X, y))
# 取出前buffer_size个数据放入buffer,并从其中随机采样,采样后的数据用后续数据替换
train_dataset = train_dataset.shuffle(buffer_size=23000)
train_dataset = train_dataset.batch(batch_size)
train_dataset = train_dataset.prefetch(tf.data.experimental.AUTOTUNE)
# 构建堆叠网络模型
rate = 0
model = tf.keras.Sequential([
# layers.Reshape(target_shape=(-1, 20, 20, 1),input_shape=[20, 20, 1]),
layers.Conv2D(filters=32, # 卷积层神经元(卷积核)数目
kernel_size=3, # 感受野大小
strides=1, # 移动补偿
padding='same', # padding策略(vaild 或 same)
activation=tf.nn.relu, # 激活函数
input_shape=[20, 20, 1]
),
# layers.Activation('relu'),
layers.MaxPool2D(pool_size=2, padding='same'),
layers.Dropout(rate),
layers.Conv2D(filters=64, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu),
layers.MaxPool2D(pool_size=2, padding='same'),
layers.Dropout(rate),
layers.Conv2D(filters=128, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu),
layers.MaxPool2D(pool_size=2, padding='same'),
layers.Dropout(rate),
layers.Reshape(target_shape=(3 * 3 * 128,)),
# layers.Dense(units=3 * 3 * 128, activation=tf.nn.relu),
# layers.Dropout(rate),
layers.Dense(units=1024, activation=tf.nn.relu),
layers.Dropout(rate),
layers.Dense(units=dataset_len),
layers.Softmax()
])
model.compile(
optimizer=tf.keras.optimizers.Adam(learning_rate=learning_rate), # 归一化器
loss=tf.keras.losses.sparse_categorical_crossentropy, # loss计算方法
metrics=[tf.keras.metrics.sparse_categorical_accuracy] # acc计算方法
)
model.summary()
callbacks = [
keras.callbacks.ModelCheckpoint( # 设置自动保存模型和acc周期回显
train_model_path,
monitor='val_acc', # 监控acc属性
verbose=1, # 显示结果
save_best_only=True, # 仅保存最好的模型
save_weights_only=False,
mode='auto',
period=10 # 检查间隔
)
]
model.fit(train_dataset, epochs=num_epochs, callbacks=callbacks)
model.save(train_model_path)
def test():
model = tf.saved_model.load(train_model_path)
X = init_testData(test_data_dir)
y_pred = model(X)
print(y_pred)
for y in y_pred:
print(dataset[np.argmax(y)])
# print(dataset[y_pred])
sparse_categorical_accuracy = tf.keras.metrics.SparseCategoricalAccuracy()
# num_batches = int(len(X) // batch_size)
# for batch_index in range(num_batches):
# start_index, end_index = batch_index * batch_size, (batch_index + 1) * batch_size
# y_pred = model(X[start_index: end_index])
# print(X)
# print(dataset[y_pred])
# sparse_categorical_accuracy.update_state(y_true=X[start_index: end_index], y_pred=y_pred)
# print("test accuracy: %f" % sparse_categorical_accuracy.result())
if __name__ == '__main__':
# train()
test()