import os
import warnings
warnings.filterwarnings("ignore")
import tensorflow as tf
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.applications.resnet import ResNet50
from pathlib import Path
import numpy as np
#数据所在文件夹
base_dir = './data/cats_and_dogs'
train_dir = Path(os.path.join(base_dir,'train'))
file_pattern = os.path.join(train_dir,'*/*.jpg')
image_count = len(list(train_dir.glob('*/*.jpg')))
list_ds = tf.data.Dataset.list_files(file_pattern,shuffle = False)
list_ds = list_ds.shuffle(image_count, reshuffle_each_iteration=False)
for f in list_ds.take(5):
print(f.numpy())
class_names = np.array(sorted([item.name for item in train_dir.glob('*') ]))
print(class_names)
val_size = int(image_count * 0.2)
train_data = list_ds.skip(val_size)
validation_data = list_ds.take(val_size)
print(tf.data.experimental.cardinality(train_data).numpy())
print(tf.data.experimental.cardinality(validation_data).numpy())
def get_label(file_path):
parts = tf.strings.split(file_path, os.path.sep)
one_hot = parts[-2] == class_names
return tf.argmax(one_hot)
def decode_img(img):
img = tf.io.decode_jpeg(img, channels=3)
return tf.image.resize(img, [64, 64])
def process_path(file_path):
label = get_label(file_path)
img = tf.io.read_file(file_path)
img = decode_img(img)
return img, label
train_data = train_data.map(process_path, num_parallel_calls=tf.data.AUTOTUNE)
validation_data = validation_data.map(process_path, num_parallel_calls=tf.data.AUTOTUNE)
for image, label in train_data.take(2):
print("Image shape: ", image.numpy().shape)
print("Label: ", label.numpy())
def configure_for_performance(ds):
ds = ds.cache()
ds = ds.shuffle(buffer_size=1000)
ds = ds.batch(4)
ds = ds.prefetch(buffer_size=tf.data.AUTOTUNE)
return ds
train_data = configure_for_performance(train_data)
validation_data = configure_for_performance(validation_data)
save_model_cb = tf.keras.callbacks.ModelCheckpoint(filepath='model_resnet50_cats_and_dogs.h5', save_freq='epoch')
base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(64, 64, 3))
base_model.trainable = True
model = tf.keras.models.Sequential([
base_model,
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(512, activation='relu',kernel_regularizer=tf.keras.regularizers.l2(l=0.01)),
tf.keras.layers.Dense(1, activation='sigmoid')
])
model.compile(loss='binary_crossentropy',optimizer = Adam(lr=1e-3),metrics = ['acc'])
history = model.fit(train_data.repeat(),steps_per_epoch=100,epochs=50,validation_data=validation_data.repeat(),validation_steps=50,verbose=1,callbacks = [save_model_cb])
