[英]VGG16 Training new dataset: Why VGG16 needs label to have shape (None,2,2,10) and how do I train mnist dataset with this network?
[英]I'm facing below issue whe I train the model using VGG16
在嘗試擬合我的模型時,我面臨以下問題:
ValueError: Input 0 of layer "model" is incompatible with the layer: expected shape=(None, 256, 96, 3), found shape=(None, 1, 8, 3, 512)
我的模型的詳細信息如下:
img_height = 96
img_width = 256
#Get back the convolutional part of a VGG network trained on ImageNet
model_vgg16_conv = VGG16(weights='imagenet', include_top=False, input_shape=(img_width, img_height, 3))
#Create your own input format (here 3x200x200)
input = Input(shape=(img_width, img_height, 3))
#Use the generated model
output_vgg16_conv = model_vgg16_conv(input)
#Add the fully-connected layers
x = Flatten(name='flatten')(output_vgg16_conv)
x = Dense(512, activation='relu', name='Dense1')(x)
x = Dropout(0.2, name = 'Dropout')(x)
x = Dense(45, activation='softmax', name='predictions')(x)
#Create your own model
my_model = Model(inputs=input, outputs=x)
#In the summary, weights and layers from the VGG part will be hidden, but they will be fit during the training
my_model.summary()
my_model.compile(
loss = 'sparse_categorical_crossentropy',
optimizer = 'adam',
metrics = ['accuracy']
)
my_model.fit(
features,
labels,
batch_size = 5,
epochs = 15,
validation_split = 0.1,
callbacks=[TensorBoard]
)
有什么建議可以調整我的模型以解決問題嗎? 請注意特征:X,標簽:y,總圖像:4193 和 4 個類別
我的數據集生成代碼:
conv_base = VGG16(
weights='imagenet',
include_top=False,
input_shape=(img_width, img_height, 3)
)
形象重塑
for input_image in tqdm(os.listdir(dir)):
try:
img = image.load_img(os.path.join(dir, input_image), target_size=(img_width, img_height))
img_tensor = image.img_to_array(img)
img_tensor /= 255.
pic = conv_base.predict(img_tensor.reshape(1, img_width, img_height, 3))
data.append([pic, index])
except Exception as e:
pass
我需要對此做任何調整嗎?
您需要確保對模型的輸入是正確的。 我正在使用隨機生成的數據tf.random.normal((64, 256, 96, 3))
,其中 64 是樣本數,256 是您的img_width
,96 是您的img_height
,3 是通道數。 另請注意,如果您有 4 個類,則您的輸出層應該有 4 個節點。
import tensorflow as tf
img_height = 96
img_width = 256
#Get back the convolutional part of a VGG network trained on ImageNet
model_vgg16_conv = tf.keras.applications.VGG16(weights='imagenet', include_top=False, input_shape=(img_width, img_height, 3))
#Create your own input format (here 3x200x200)
input = tf.keras.layers.Input(shape=(img_width, img_height, 3))
#Use the generated model
output_vgg16_conv = model_vgg16_conv(input)
#Add the fully-connected layers
x = tf.keras.layers.Flatten(name='flatten')(output_vgg16_conv)
x = tf.keras.layers.Dense(512, activation='relu', name='Dense1')(x)
x = tf.keras.layers.Dropout(0.2, name = 'Dropout')(x)
x = tf.keras.layers.Dense(4, activation='softmax', name='predictions')(x)
#Create your own model
my_model = tf.keras.Model(inputs=input, outputs=x)
#In the summary, weights and layers from the VGG part will be hidden, but they will be fit during the training
my_model.summary()
my_model.compile(
loss = 'sparse_categorical_crossentropy',
optimizer = 'adam',
metrics = ['accuracy']
)
my_model.fit(
tf.random.normal((64, 256, 96, 3)),
tf.random.uniform((64, 1), maxval=4),
batch_size = 5,
epochs = 15)
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