使用初始网络对迁移学习模型进行预处理
Preprocessing for Transfer Learning Model with an Inception Network
我正在尝试使用 Inception Network 作为基础来构建图像分类模型。这是一个简单的二元分类模型。
我的图像可以在一个大目录中的许多小目录中找到。他们每个人都有自己的 'image id'，这就是他们的命名方式。除此之外，我还有一些 tsv 文件，其中包含这些图像 ID 和相应的标签（'Positive' 或 'Negative'）。
当我训练模型时，我发现我的准确性波动很大，没有太大进步。我想知道我准备数据集的方式是否有任何问题。我为此编写了一些函数。
在使用这些函数之前，下面给出了我定义模型的方式，
base_model = InceptionV3(weights='imagenet', include_top=False)
x = base_model.output
x = GlobalAveragePooling2D(name='avg_pool')(x)
x = Dropout(0.4)(x)
predictions = Dense(2, activation='sigmoid')(x)
model = Model(inputs=base_model.input, outputs=predictions)
for layer in base_model.layers:
layer.trainable = False
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
这些是我为了准备数据而编写的函数，
def vectorize_img(img_path):
img = load_img(img_path, target_size=(224, 224)) #size is 224,224 by default
x = img_to_array(img) #change to np array
x = preprocess_input(x) #make input confirm to InceptionV3 input format
return x
def prepare_features(base_dir, limit):
features_dict = dict()
for dir1 in os.listdir(base_dir):
for dir2 in os.listdir(base_dir + dir1):
for file in os.listdir(base_dir + dir1 + '/' + dir2):
if len(features_dict) < limit:
try:
img_path = base_dir + dir1 + '/' + dir2 + '/' + file
x = vectorize_img(img_path)
name_id = file.split('.')[0] #take the file name and use as id in dict
features_dict[name_id] = x
except Exception as e:
print(e)
pass
return features_dict
def prepare_data(file_path, features_dict):
inputs = []
labels = []
df = pd.read_csv(file_path, sep='\t')
df = df[['image_id', 'label_text_image']]
df['class'] = df[['image_id', 'label_text_image']].apply(lambda x: 1 if x['label_text_image'] == 'Positive' else 0, axis = 1)
for index, row in df.iterrows():
try:
inputs.append(features_dict[row['image_id']])
labels.append(row['class'])
except:
pass
return np.asarray(inputs), tf.one_hot(np.asarray(labels), depth=2)
然后调用这些函数来准备我的数据集，
features_dict = prepare_features('/path/to/img/dir', 8000)
x_train, y_train = prepare_data('/path/to/train/tsv', features_dict)
x_dev, y_dev = prepare_data('/path/to/dev/tsv', features_dict)
x_test, y_test = prepare_data('/path/to/test/tsv', features_dict)
模型终于训练好了，
EPOCHS = 50
BATCH_SIZE = 32
STEPS_PER_EPOCH = 1
history = model.fit(x=x_train, y=y_train, validation_data=(x_dev, y_dev), epochs=EPOCHS, steps_per_epoch=STEPS_PER_EPOCH, batch_size=BATCH_SIZE)
model.evaluate(x=x_test, y=y_test, batch_size=BATCH_SIZE)
我是不是做错了什么？
这是我的模型取得的结果，
Epoch 1/50
1/1 [==============================] - 158s 158s/step - loss: 0.8298 - accuracy: 0.5000 - val_loss: 0.7432 - val_accuracy: 0.5227
Epoch 2/50
1/1 [==============================] - 113s 113s/step - loss: 0.7775 - accuracy: 0.4688 - val_loss: 0.8225 - val_accuracy: 0.5153
Epoch 3/50
1/1 [==============================] - 113s 113s/step - loss: 0.7663 - accuracy: 0.5625 - val_loss: 0.8431 - val_accuracy: 0.5174
Epoch 4/50
1/1 [==============================] - 156s 156s/step - loss: 1.1292 - accuracy: 0.5312 - val_loss: 0.7763 - val_accuracy: 0.5227
Epoch 5/50
1/1 [==============================] - 114s 114s/step - loss: 0.7452 - accuracy: 0.5312 - val_loss: 0.7332 - val_accuracy: 0.5448
Epoch 6/50
1/1 [==============================] - 156s 156s/step - loss: 0.7884 - accuracy: 0.5312 - val_loss: 0.7072 - val_accuracy: 0.5606
Epoch 7/50
1/1 [==============================] - 114s 114s/step - loss: 0.7856 - accuracy: 0.5312 - val_loss: 0.7195 - val_accuracy: 0.5764
Epoch 8/50
1/1 [==============================] - 156s 156s/step - loss: 0.9203 - accuracy: 0.5312 - val_loss: 0.7348 - val_accuracy: 0.5616
Epoch 9/50
1/1 [==============================] - 156s 156s/step - loss: 0.8639 - accuracy: 0.4062 - val_loss: 0.7275 - val_accuracy: 0.5690
Epoch 10/50
1/1 [==============================] - 156s 156s/step - loss: 0.6170 - accuracy: 0.7188 - val_loss: 0.7125 - val_accuracy: 0.5880
Epoch 11/50
1/1 [==============================] - 156s 156s/step - loss: 0.5756 - accuracy: 0.7188 - val_loss: 0.6979 - val_accuracy: 0.6017
Epoch 12/50
1/1 [==============================] - 113s 113s/step - loss: 0.9976 - accuracy: 0.4375 - val_loss: 0.6834 - val_accuracy: 0.5933
Epoch 13/50
1/1 [==============================] - 156s 156s/step - loss: 0.7025 - accuracy: 0.5938 - val_loss: 0.6863 - val_accuracy: 0.5838
您提到它是二元分类，因此标签是 {0,1}。在这种情况下，您的模型输出应该是
predictions = Dense(2, activation='softmax')(x)
带有分类标签[0,1] or [1,0]
或者
predictions = Dense(1, activation='sigmoid')(x)
带有二进制标签 1 or 0
但您使用的输出 2 与 sigmoid 即 predictions = Dense(2, activation='sigmoid')(x).
我正在尝试使用 Inception Network 作为基础来构建图像分类模型。这是一个简单的二元分类模型。
我的图像可以在一个大目录中的许多小目录中找到。他们每个人都有自己的 'image id'，这就是他们的命名方式。除此之外，我还有一些 tsv 文件，其中包含这些图像 ID 和相应的标签（'Positive' 或 'Negative'）。
当我训练模型时，我发现我的准确性波动很大，没有太大进步。我想知道我准备数据集的方式是否有任何问题。我为此编写了一些函数。
在使用这些函数之前，下面给出了我定义模型的方式，
base_model = InceptionV3(weights='imagenet', include_top=False)
x = base_model.output
x = GlobalAveragePooling2D(name='avg_pool')(x)
x = Dropout(0.4)(x)
predictions = Dense(2, activation='sigmoid')(x)
model = Model(inputs=base_model.input, outputs=predictions)
for layer in base_model.layers:
layer.trainable = False
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
这些是我为了准备数据而编写的函数，
def vectorize_img(img_path):
img = load_img(img_path, target_size=(224, 224)) #size is 224,224 by default
x = img_to_array(img) #change to np array
x = preprocess_input(x) #make input confirm to InceptionV3 input format
return x
def prepare_features(base_dir, limit):
features_dict = dict()
for dir1 in os.listdir(base_dir):
for dir2 in os.listdir(base_dir + dir1):
for file in os.listdir(base_dir + dir1 + '/' + dir2):
if len(features_dict) < limit:
try:
img_path = base_dir + dir1 + '/' + dir2 + '/' + file
x = vectorize_img(img_path)
name_id = file.split('.')[0] #take the file name and use as id in dict
features_dict[name_id] = x
except Exception as e:
print(e)
pass
return features_dict
def prepare_data(file_path, features_dict):
inputs = []
labels = []
df = pd.read_csv(file_path, sep='\t')
df = df[['image_id', 'label_text_image']]
df['class'] = df[['image_id', 'label_text_image']].apply(lambda x: 1 if x['label_text_image'] == 'Positive' else 0, axis = 1)
for index, row in df.iterrows():
try:
inputs.append(features_dict[row['image_id']])
labels.append(row['class'])
except:
pass
return np.asarray(inputs), tf.one_hot(np.asarray(labels), depth=2)
然后调用这些函数来准备我的数据集，
features_dict = prepare_features('/path/to/img/dir', 8000)
x_train, y_train = prepare_data('/path/to/train/tsv', features_dict)
x_dev, y_dev = prepare_data('/path/to/dev/tsv', features_dict)
x_test, y_test = prepare_data('/path/to/test/tsv', features_dict)
模型终于训练好了，
EPOCHS = 50
BATCH_SIZE = 32
STEPS_PER_EPOCH = 1
history = model.fit(x=x_train, y=y_train, validation_data=(x_dev, y_dev), epochs=EPOCHS, steps_per_epoch=STEPS_PER_EPOCH, batch_size=BATCH_SIZE)
model.evaluate(x=x_test, y=y_test, batch_size=BATCH_SIZE)
我是不是做错了什么？
这是我的模型取得的结果，
Epoch 1/50
1/1 [==============================] - 158s 158s/step - loss: 0.8298 - accuracy: 0.5000 - val_loss: 0.7432 - val_accuracy: 0.5227
Epoch 2/50
1/1 [==============================] - 113s 113s/step - loss: 0.7775 - accuracy: 0.4688 - val_loss: 0.8225 - val_accuracy: 0.5153
Epoch 3/50
1/1 [==============================] - 113s 113s/step - loss: 0.7663 - accuracy: 0.5625 - val_loss: 0.8431 - val_accuracy: 0.5174
Epoch 4/50
1/1 [==============================] - 156s 156s/step - loss: 1.1292 - accuracy: 0.5312 - val_loss: 0.7763 - val_accuracy: 0.5227
Epoch 5/50
1/1 [==============================] - 114s 114s/step - loss: 0.7452 - accuracy: 0.5312 - val_loss: 0.7332 - val_accuracy: 0.5448
Epoch 6/50
1/1 [==============================] - 156s 156s/step - loss: 0.7884 - accuracy: 0.5312 - val_loss: 0.7072 - val_accuracy: 0.5606
Epoch 7/50
1/1 [==============================] - 114s 114s/step - loss: 0.7856 - accuracy: 0.5312 - val_loss: 0.7195 - val_accuracy: 0.5764
Epoch 8/50
1/1 [==============================] - 156s 156s/step - loss: 0.9203 - accuracy: 0.5312 - val_loss: 0.7348 - val_accuracy: 0.5616
Epoch 9/50
1/1 [==============================] - 156s 156s/step - loss: 0.8639 - accuracy: 0.4062 - val_loss: 0.7275 - val_accuracy: 0.5690
Epoch 10/50
1/1 [==============================] - 156s 156s/step - loss: 0.6170 - accuracy: 0.7188 - val_loss: 0.7125 - val_accuracy: 0.5880
Epoch 11/50
1/1 [==============================] - 156s 156s/step - loss: 0.5756 - accuracy: 0.7188 - val_loss: 0.6979 - val_accuracy: 0.6017
Epoch 12/50
1/1 [==============================] - 113s 113s/step - loss: 0.9976 - accuracy: 0.4375 - val_loss: 0.6834 - val_accuracy: 0.5933
Epoch 13/50
1/1 [==============================] - 156s 156s/step - loss: 0.7025 - accuracy: 0.5938 - val_loss: 0.6863 - val_accuracy: 0.5838
您提到它是二元分类，因此标签是 {0,1}。在这种情况下，您的模型输出应该是
predictions = Dense(2, activation='softmax')(x)
带有分类标签[0,1] or [1,0]
或者
predictions = Dense(1, activation='sigmoid')(x)
带有二进制标签 1 or 0
但您使用的输出 2 与 sigmoid 即 predictions = Dense(2, activation='sigmoid')(x).