KerasLayer vs tf.keras.applications性能

Question

我已经用ResNetV2 50 ( https://tfhub.dev/google/imagenet/resnet_v2_50/feature_vector/4 )训练了一些网络，它对我的数据集非常有效。

然后我尝试了tf.keras.applications.ResNet50，准确率比其他的要低得多。

这里有两个模型：

第一个(带集线器)

base_model = hub.KerasLayer('https://tfhub.dev/google/imagenet/resnet_v2_50/feature_vector/4', input_shape=(IMAGE_H, IMAGE_W, 3))
base_model.trainable = False

model = tf.keras.Sequential([
  base_model ,
  Dense(num_classes, activation='softmax')
])

第二个(使用keras.applications)

base_model = tf.keras.applications.ResNet50V2(input_shape=(IMAGE_H, IMAGE_W, 3), include_top=False, weights='imagenet', pooling='avg')
base_model.trainable = False

model = tf.keras.Sequential([
  base_model,
  Dense(num_classes, activation='softmax')
])

优化器是相同的(Adam)，时期，步骤，数据集(训练和验证)，学习率也是相同的。但第一次开始时val_accuracy接近80%，结束时准确率接近99%，第二次开始时从第一个时期到最后一个时期85%的val_accuracy，因为它过度拟合。我得到了相同的行为，改变了每个模型的数据集和参数。

我做错了什么？

Answer 1

ResNet50V2的tf.keras.applications.ResNet50V2和hub.KerasLayer都是在相同的image_net数据集上训练的，并且具有相同的权重，两者之间没有区别。

谈到精度差异，我已经尝试了API加载基础模型和在10个时期上运行。

我看到了训练和验证的准确性上的细微差别。

然后，在第二次运行TensorFlow集线器基础模型后，我再次发现accuracy.This中的一些微小变化可能是由于这些计算的顺序造成的，也可能是浮点精度错误。

以下是输出：

作为基础模型的tf.keras.applications.ResNet50V2。

base_model = tf.keras.applications.ResNet50V2(input_shape=(96, 96, 3), include_top=False, weights='imagenet', pooling='avg')
base_model.trainable = False

model = tf.keras.Sequential([
  base_model,
  Dense(10, activation='softmax')
]) 
model.compile(loss="categorical_crossentropy",optimizer="adam",metrics=["accuracy"])
H = model.fit_generator(aug.flow(trainX, trainY, batch_size=16),
    validation_data=(testX, testY), steps_per_epoch=len(trainX) // 16,
    epochs=10)  

输出1:

Epoch 1/10
103/103 [==============================] - 55s 535ms/step - loss: 1.5820 - accuracy: 0.4789 - val_loss: 0.9162 - val_accuracy: 0.6949
Epoch 2/10
103/103 [==============================] - 57s 554ms/step - loss: 0.9539 - accuracy: 0.6534 - val_loss: 0.8376 - val_accuracy: 0.6852
Epoch 3/10
103/103 [==============================] - 55s 532ms/step - loss: 0.8610 - accuracy: 0.6944 - val_loss: 0.7104 - val_accuracy: 0.7240
Epoch 4/10
103/103 [==============================] - 55s 533ms/step - loss: 0.7671 - accuracy: 0.7214 - val_loss: 0.5988 - val_accuracy: 0.7918
Epoch 5/10
103/103 [==============================] - 55s 536ms/step - loss: 0.6994 - accuracy: 0.7526 - val_loss: 0.6029 - val_accuracy: 0.7676
Epoch 6/10
103/103 [==============================] - 55s 537ms/step - loss: 0.6880 - accuracy: 0.7508 - val_loss: 0.6121 - val_accuracy: 0.7724
Epoch 7/10
103/103 [==============================] - 55s 533ms/step - loss: 0.6588 - accuracy: 0.7593 - val_loss: 0.5486 - val_accuracy: 0.8015
Epoch 8/10
103/103 [==============================] - 55s 534ms/step - loss: 0.6640 - accuracy: 0.7630 - val_loss: 0.5287 - val_accuracy: 0.8232
Epoch 9/10
103/103 [==============================] - 54s 528ms/step - loss: 0.6004 - accuracy: 0.7881 - val_loss: 0.4598 - val_accuracy: 0.8426
Epoch 10/10
103/103 [==============================] - 55s 530ms/step - loss: 0.5583 - accuracy: 0.8016 - val_loss: 0.4605 - val_accuracy: 0.8426

Tensorflow Hub作为基础模型。

tf.keras.backend.clear_session()
np.random.seed(42)
tf.random.set_seed(42) 
base_model =hub.KerasLayer('https://tfhub.dev/google/imagenet/resnet_v2_50/feature_vector/4', input_shape=(96, 96, 3))
base_model.trainable = False

model = tf.keras.Sequential([base_model ,Dense(10, activation='softmax')]) 
model.compile(loss="categorical_crossentropy",optimizer="adam",metrics=["accuracy"])
H = model.fit_generator(aug.flow(trainX, trainY, batch_size=16),
    validation_data=(testX, testY), steps_per_epoch=len(trainX) // 16,
    epochs=10)

输出2:

Epoch 1/10
103/103 [==============================] - 54s 526ms/step - loss: 1.7543 - accuracy: 0.4464 - val_loss: 1.0185 - val_accuracy: 0.5981
Epoch 2/10
103/103 [==============================] - 53s 519ms/step - loss: 0.9827 - accuracy: 0.6283 - val_loss: 1.0067 - val_accuracy: 0.6416
Epoch 3/10
103/103 [==============================] - 56s 548ms/step - loss: 0.8719 - accuracy: 0.6944 - val_loss: 0.7195 - val_accuracy: 0.7240
Epoch 4/10
103/103 [==============================] - 54s 521ms/step - loss: 0.8177 - accuracy: 0.7208 - val_loss: 0.7490 - val_accuracy: 0.7385
Epoch 5/10
103/103 [==============================] - 54s 522ms/step - loss: 0.7641 - accuracy: 0.7379 - val_loss: 0.6325 - val_accuracy: 0.7797
Epoch 6/10
103/103 [==============================] - 54s 523ms/step - loss: 0.6551 - accuracy: 0.7655 - val_loss: 0.6431 - val_accuracy: 0.7579
Epoch 7/10
103/103 [==============================] - 55s 530ms/step - loss: 0.6538 - accuracy: 0.7734 - val_loss: 0.5824 - val_accuracy: 0.7797
Epoch 8/10
103/103 [==============================] - 55s 536ms/step - loss: 0.6387 - accuracy: 0.7691 - val_loss: 0.6254 - val_accuracy: 0.7772
Epoch 9/10
103/103 [==============================] - 56s 540ms/step - loss: 0.6394 - accuracy: 0.7685 - val_loss: 0.6539 - val_accuracy: 0.7554
Epoch 10/10
103/103 [==============================] - 55s 536ms/step - loss: 0.5816 - accuracy: 0.7955 - val_loss: 0.5703 - val_accuracy: 0.7990  

现在如果我再次运行Tensorflow集线器模型。

输出3:

Epoch 1/10
103/103 [==============================] - 55s 534ms/step - loss: 1.6412 - accuracy: 0.4764 - val_loss: 1.0697 - val_accuracy: 0.5738
Epoch 2/10
103/103 [==============================] - 54s 528ms/step - loss: 1.0312 - accuracy: 0.6412 - val_loss: 1.0196 - val_accuracy: 0.6077
Epoch 3/10
103/103 [==============================] - 59s 570ms/step - loss: 0.8710 - accuracy: 0.6975 - val_loss: 0.7088 - val_accuracy: 0.7240
Epoch 4/10
103/103 [==============================] - 54s 529ms/step - loss: 0.8108 - accuracy: 0.7128 - val_loss: 0.6539 - val_accuracy: 0.7458
Epoch 5/10
103/103 [==============================] - 54s 522ms/step - loss: 0.7311 - accuracy: 0.7440 - val_loss: 0.6029 - val_accuracy: 0.7676
Epoch 6/10
103/103 [==============================] - 54s 523ms/step - loss: 0.6683 - accuracy: 0.7612 - val_loss: 0.6621 - val_accuracy: 0.7506
Epoch 7/10
103/103 [==============================] - 54s 527ms/step - loss: 0.6518 - accuracy: 0.7753 - val_loss: 0.6166 - val_accuracy: 0.7700
Epoch 8/10
103/103 [==============================] - 54s 524ms/step - loss: 0.6147 - accuracy: 0.7795 - val_loss: 0.5611 - val_accuracy: 0.7797
Epoch 9/10
103/103 [==============================] - 54s 522ms/step - loss: 0.6671 - accuracy: 0.7667 - val_loss: 0.5126 - val_accuracy: 0.8087
Epoch 10/10
103/103 [==============================] - 54s 525ms/step - loss: 0.6090 - accuracy: 0.7832 - val_loss: 0.6355 - val_accuracy: 0.7627  

希望这能回答你的问题。