訓練tensorflow使用VGG16對狗/貓進行分類，但準確率非常低-有解無憂

這是我的完整代碼：

from tensorflow.keras.preprocessing.image import load_img
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.applications.vgg16 import preprocess_input
from tensorflow.keras.applications.vgg16 import decode_predictions
from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.models import load_model
from tensorflow.keras.layers import Dense, Flatten, Dropout, Input
from tensorflow.keras.models import Model
import numpy as np
from os import listdir
from os.path import isfile, join
import matplotlib.pyplot as plt

train_cats_path = r"C:\Users\jrand\Downloads\dogscats\train\cats"
train_cats = [train_cats_path "\\" f for f in listdir(train_cats_path) if isfile(join(train_cats_path, f))]
train_cat_array = []
for tmp_cat in train_cats:
    img = load_img(tmp_cat, target_size=(224, 224)) # Load image in with desired dimensions
    img = img_to_array(img) # Convert the image to a numpy array
    img = img.reshape(224, 224, 3)
    img = preprocess_input(img)
    train_cat_array.append(img)
x_train_cat_labels = np.array([1.0]*len(train_cat_array))
x_train_cat_images = np.array(train_cat_array)
train_dogs_path = r"C:\Users\jrand\Downloads\dogscats\train\cats"
train_dogs = [train_dogs_path "\\" f for f in listdir(train_dogs_path) if isfile(join(train_dogs_path, f))]
train_dog_array = []
for tmp_dog in train_dogs:
    img = load_img(tmp_dog, target_size=(224, 224)) # Load image in with desired dimensions
    img = img_to_array(img) # Convert the image to a numpy array
    img = img.reshape(224, 224, 3)
    img = preprocess_input(img)
    train_dog_array.append(img)
x_train_dog_labels = np.array([0.0]*len(train_dog_array))
x_train_dog_images = np.array(train_dog_array)

print("len of dog images", len(x_train_dog_images), "len of cat images", len(x_train_cat_images))
print("len of dog labels", len(x_train_dog_labels), "len of cat labels", len(x_train_cat_labels))

x_train_images = np.concatenate([x_train_dog_images, x_train_cat_images])[0:1000]
x_train_labels =  np.concatenate([x_train_dog_labels, x_train_cat_labels])[0:1000]

model = VGG16(weights = 'imagenet', include_top = False, input_shape = (224, 224, 3))
for layer in model.layers:
    layer.trainable = False
x = Flatten()(model.output)
predictions = Dense(1, activation="sigmoid")(x)
new_model = Model(model.input, predictions)



# compile model
model.compile(optimizer="Adam", loss="binary_crossentropy", metrics=["accuracy"])
# train model
history = model.fit(x_train_images, x_train_labels, batch_size=1, epochs=100)

因此，如您所見，我拍攝貓和狗的影像，為它們生成標簽，然后使用 numpy 將兩個陣列連接在一起，以便我可以對它們進行訓練。

我想我的問題如下：

我必須通過將陣列拼接到前 1000 張影像來減小訓練集的大小，這無濟于事
我必須使用 1 的 batch_size，也無濟于事

誰能給我一些改進此代碼和 NN 性能的提示？

這是當前情況的示例輸出：

len of dog images 11500 len of cat images 11500
len of dog labels 11500 len of cat labels 11500
2022-03-04 11:46:54.410085: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-03-04 11:46:55.170021: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 6613 MB memory:  -> device: 0, name: NVIDIA GeForce GTX 1080, pci bus id: 0000:02:00.0, compute capability: 6.1
2022-03-04 11:46:55.170948: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 6613 MB memory:  -> device: 1, name: NVIDIA GeForce GTX 1080, pci bus id: 0000:04:00.0, compute capability: 6.1
2022-03-04 11:46:56.198632: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)
Epoch 1/100
2022-03-04 11:46:56.725619: I tensorflow/stream_executor/cuda/cuda_dnn.cc:369] Loaded cuDNN version 8201
1000/1000 [==============================] - 9s 7ms/step - loss: 2.1102 - accuracy: 1.0204e-04
Epoch 2/100
1000/1000 [==============================] - 7s 7ms/step - loss: 2.1102 - accuracy: 1.0204e-04
Epoch 3/100
1000/1000 [==============================] - 7s 7ms/step - loss: 2.1102 - accuracy: 1.0204e-04
Epoch 4/100
1000/1000 [==============================] - 7s 7ms/step - loss: 2.1102 - accuracy: 1.0204e-04
Epoch 5/100
1000/1000 [==============================] - 7s 7ms/step - loss: 2.1102 - accuracy: 1.0204e-04
Epoch 6/100
1000/1000 [==============================] - 7s 7ms/step - loss: 2.1102 - accuracy: 1.0204e-04
Epoch 7/100
1000/1000 [==============================] - 7s 7ms/step - loss: 2.1102 - accuracy: 1.0204e-04
Epoch 8/100
1000/1000 [==============================] - 7s 7ms/step - loss: 2.1102 - accuracy: 1.0204e-04
Epoch 9/100
1000/1000 [==============================] - 7s 7ms/step - loss: 2.1102 - accuracy: 1.0204e-04
Epoch 10/100
1000/1000 [==============================] - 7s 7ms/step - loss: 2.1102 - accuracy: 1.0204e-04
Epoch 11/100
1000/1000 [==============================] - 7s 7ms/step - loss: 2.1102 - accuracy: 1.0204e-04
Epoch 12/100
1000/1000 [==============================] - 7s 7ms/step - loss: 2.1102 - accuracy: 1.0204e-04
Epoch 13/100
1000/1000 [==============================] - 7s 7ms/step - loss: 2.1102 - accuracy: 1.0204e-04
Epoch 14/100
1000/1000 [==============================] - 7s 7ms/step - loss: 2.1102 - accuracy: 1.0204e-04
Epoch 15/100
1000/1000 [==============================] - 7s 7ms/step - loss: 2.1102 - accuracy: 1.0204e-04
Epoch 16/100
1000/1000 [==============================] - 7s 7ms/step - loss: 2.1102 - accuracy: 1.0204e-04
Epoch 17/100
1000/1000 [==============================] - 7s 7ms/step - loss: 2.1102 - accuracy: 1.0204e-04
Epoch 18/100
1000/1000 [==============================] - 7s 7ms/step - loss: 2.1102 - accuracy: 1.0204e-04
Epoch 19/100
1000/1000 [==============================] - 7s 7ms/step - loss: 2.1102 - accuracy: 1.0204e-04
Epoch 20/100
1000/1000 [==============================] - 7s 7ms/step - loss: 2.1102 - accuracy: 1.0204e-04
Epoch 21/100
1000/1000 [==============================] - 7s 7ms/step - loss: 2.1102 - accuracy: 1.0204e-04
Epoch 22/100
1000/1000 [==============================] - 7s 7ms/step - loss: 2.1102 - accuracy: 1.0204e-04
Epoch 23/100
1000/1000 [==============================] - 7s 7ms/step - loss: 2.1102 - accuracy: 1.0204e-04
Epoch 24/100
1000/1000 [==============================] - 7s 7ms/step - loss: 2.1102 - accuracy: 1.0204e-04
Epoch 25/100
1000/1000 [==============================] - 7s 7ms/step - loss: 2.1102 - accuracy: 1.0204e-04
Epoch 26/100
1000/1000 [==============================] - 7s 7ms/step - loss: 2.1102 - accuracy: 1.0204e-04
Epoch 27/100
1000/1000 [==============================] - 7s 7ms/step - loss: 2.1102 - accuracy: 1.0204e-04
Epoch 28/100
1000/1000 [==============================] - 7s 7ms/step - loss: 2.1102 - accuracy: 1.0204e-04
Epoch 29/100
1000/1000 [==============================] - 7s 7ms/step - loss: 2.1102 - accuracy: 1.0204e-04
Epoch 30/100
1000/1000 [==============================] - 7s 7ms/step - loss: 2.1102 - accuracy: 1.0204e-04
Epoch 31/100
1000/1000 [==============================] - 7s 7ms/step - loss: 2.1102 - accuracy: 1.0204e-04
Epoch 32/100
1000/1000 [==============================] - 7s 7ms/step - loss: 2.1102 - accuracy: 1.0204e-04
Epoch 33/100
1000/1000 [==============================] - 7s 7ms/step - loss: 2.1102 - accuracy: 1.0204e-04
Epoch 34/100
1000/1000 [==============================] - 7s 7ms/step - loss: 2.1102 - accuracy: 1.0204e-04
Epoch 35/100
1000/1000 [==============================] - 7s 7ms/step - loss: 2.1102 - accuracy: 1.0204e-04
Epoch 36/100
1000/1000 [==============================] - 7s 7ms/step - loss: 2.1102 - accuracy: 1.0204e-04
Epoch 37/100
1000/1000 [==============================] - 7s 7ms/step - loss: 2.1102 - accuracy: 1.0204e-04
Epoch 38/100
1000/1000 [==============================] - 7s 7ms/step - loss: 2.1102 - accuracy: 1.0204e-04
Epoch 39/100
1000/1000 [==============================] - 7s 7ms/step - loss: 2.1102 - accuracy: 1.0204e-04
Epoch 40/100

我撰寫這個程式的原因是為了獲得一些遷移學習的經驗。可能有更好的方法來實作這一點，但這是我選擇的方式。

我更新了代碼并洗掉了一個我搞砸的回圈。對于那個很抱歉。

uj5u.com熱心網友回復：

我不認為問題出在小資料集上，因為遷移學習用于處理較小的資料集。

問題是您凍結了預訓練模型 (VGG) 的所有層，而沒有添加任何新的密集層。然后呼叫 model.fit，但沒有一個層是可訓練的。因此，不允許任何改變。實際上，您的問題不在于您的準確性非常低，而是準確性在各個時期之間根本沒有變化。這應該是一個危險信號，表示您的代碼中的某些內容已損壞！

嘗試在最后一個之前添加至少另一個密集層。

編輯：

您還在model而不是new_model上編譯和呼叫 fit() 。

我希望我有幫助

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