Tensorflow 为什么固定网络输入大小会减少模型'；s文件大小
我有一个模型，它在训练期间的输入大小是可变的，用于推广 为了量化，我必须固定输入大小，所以我只需使用固定的输入大小重新创建模型，然后复制所有权重和偏差，然后保存模型。 然而，由于某种原因，模型的大小大致是四分之一。 注意，这是在量化或其他任何事情之前，参数保持不变 以下是两个模型摘要： 型号1=4.6MBTensorflow 为什么固定网络输入大小会减少模型'；s文件大小,tensorflow,keras,Tensorflow,Keras,我有一个模型，它在训练期间的输入大小是可变的，用于推广 为了量化，我必须固定输入大小，所以我只需使用固定的输入大小重新创建模型，然后复制所有权重和偏差，然后保存模型。 然而，由于某种原因，模型的大小大致是四分之一。 注意，这是在量化或其他任何事情之前，参数保持不变 以下是两个模型摘要： 型号1=4.6MB old_model.summary(line_length=110) _________________________________________________________
old_model.summary(line_length=110)
______________________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==============================================================================================================
input_1 (InputLayer) (None, None, None, 4) 0
______________________________________________________________________________________________________________
gaussian_noise_1 (GaussianNoise) (None, None, None, 4) 0 input_1[0][0]
______________________________________________________________________________________________________________
conv2d_1 (Conv2D) (None, None, None, 32) 1184 gaussian_noise_1[0][0]
______________________________________________________________________________________________________________
batch_normalization_1 (BatchNormali (None, None, None, 32) 128 conv2d_1[0][0]
______________________________________________________________________________________________________________
gaussian_noise_2 (GaussianNoise) (None, None, None, 32) 0 batch_normalization_1[0][0]
______________________________________________________________________________________________________________
conv2d_2 (Conv2D) (None, None, None, 32) 9248 gaussian_noise_2[0][0]
______________________________________________________________________________________________________________
batch_normalization_2 (BatchNormali (None, None, None, 32) 128 conv2d_2[0][0]
______________________________________________________________________________________________________________
gaussian_noise_3 (GaussianNoise) (None, None, None, 32) 0 batch_normalization_2[0][0]
______________________________________________________________________________________________________________
conv2d_3 (Conv2D) (None, None, None, 64) 18496 gaussian_noise_3[0][0]
______________________________________________________________________________________________________________
batch_normalization_3 (BatchNormali (None, None, None, 64) 256 conv2d_3[0][0]
______________________________________________________________________________________________________________
gaussian_noise_4 (GaussianNoise) (None, None, None, 64) 0 batch_normalization_3[0][0]
______________________________________________________________________________________________________________
conv2d_4 (Conv2D) (None, None, None, 64) 36928 gaussian_noise_4[0][0]
______________________________________________________________________________________________________________
batch_normalization_4 (BatchNormali (None, None, None, 64) 256 conv2d_4[0][0]
______________________________________________________________________________________________________________
gaussian_noise_5 (GaussianNoise) (None, None, None, 64) 0 batch_normalization_4[0][0]
______________________________________________________________________________________________________________
up_sampling2d_1 (UpSampling2D) (None, None, None, 64) 0 gaussian_noise_5[0][0]
______________________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, None, None, 96) 0 up_sampling2d_1[0][0]
batch_normalization_1[0][0]
______________________________________________________________________________________________________________
gaussian_noise_6 (GaussianNoise) (None, None, None, 96) 0 concatenate_1[0][0]
______________________________________________________________________________________________________________
conv2d_5 (Conv2D) (None, None, None, 64) 55360 gaussian_noise_6[0][0]
______________________________________________________________________________________________________________
batch_normalization_5 (BatchNormali (None, None, None, 64) 256 conv2d_5[0][0]
______________________________________________________________________________________________________________
gaussian_noise_7 (GaussianNoise) (None, None, None, 64) 0 batch_normalization_5[0][0]
______________________________________________________________________________________________________________
conv2d_6 (Conv2D) (None, None, None, 64) 36928 gaussian_noise_7[0][0]
______________________________________________________________________________________________________________
batch_normalization_6 (BatchNormali (None, None, None, 64) 256 conv2d_6[0][0]
______________________________________________________________________________________________________________
gaussian_noise_8 (GaussianNoise) (None, None, None, 64) 0 batch_normalization_6[0][0]
______________________________________________________________________________________________________________
conv2d_7 (Conv2D) (None, None, None, 64) 36928 gaussian_noise_8[0][0]
______________________________________________________________________________________________________________
batch_normalization_7 (BatchNormali (None, None, None, 64) 256 conv2d_7[0][0]
______________________________________________________________________________________________________________
gaussian_noise_9 (GaussianNoise) (None, None, None, 64) 0 batch_normalization_7[0][0]
______________________________________________________________________________________________________________
conv2d_8 (Conv2D) (None, None, None, 64) 36928 gaussian_noise_9[0][0]
______________________________________________________________________________________________________________
batch_normalization_8 (BatchNormali (None, None, None, 64) 256 conv2d_8[0][0]
______________________________________________________________________________________________________________
gaussian_noise_10 (GaussianNoise) (None, None, None, 64) 0 batch_normalization_8[0][0]
______________________________________________________________________________________________________________
conv2d_9 (Conv2D) (None, None, None, 64) 36928 gaussian_noise_10[0][0]
______________________________________________________________________________________________________________
batch_normalization_9 (BatchNormali (None, None, None, 64) 256 conv2d_9[0][0]
______________________________________________________________________________________________________________
gaussian_noise_11 (GaussianNoise) (None, None, None, 64) 0 batch_normalization_9[0][0]
______________________________________________________________________________________________________________
up_sampling2d_2 (UpSampling2D) (None, None, None, 64) 0 gaussian_noise_11[0][0]
______________________________________________________________________________________________________________
input_2 (InputLayer) (None, None, None, 3) 0
______________________________________________________________________________________________________________
concatenate_2 (Concatenate) (None, None, None, 67) 0 up_sampling2d_2[0][0]
input_2[0][0]
______________________________________________________________________________________________________________
gaussian_noise_12 (GaussianNoise) (None, None, None, 67) 0 concatenate_2[0][0]
______________________________________________________________________________________________________________
conv2d_10 (Conv2D) (None, None, None, 67) 40468 gaussian_noise_12[0][0]
______________________________________________________________________________________________________________
batch_normalization_10 (BatchNormal (None, None, None, 67) 268 conv2d_10[0][0]
______________________________________________________________________________________________________________
gaussian_noise_13 (GaussianNoise) (None, None, None, 67) 0 batch_normalization_10[0][0]
______________________________________________________________________________________________________________
conv2d_11 (Conv2D) (None, None, None, 67) 40468 gaussian_noise_13[0][0]
______________________________________________________________________________________________________________
batch_normalization_11 (BatchNormal (None, None, None, 67) 268 conv2d_11[0][0]
______________________________________________________________________________________________________________
gaussian_noise_14 (GaussianNoise) (None, None, None, 67) 0 batch_normalization_11[0][0]
______________________________________________________________________________________________________________
conv2d_12 (Conv2D) (None, None, None, 32) 19328 gaussian_noise_14[0][0]
______________________________________________________________________________________________________________
batch_normalization_12 (BatchNormal (None, None, None, 32) 128 conv2d_12[0][0]
______________________________________________________________________________________________________________
gaussian_noise_15 (GaussianNoise) (None, None, None, 32) 0 batch_normalization_12[0][0]
______________________________________________________________________________________________________________
conv2d_13 (Conv2D) (None, None, None, 3) 867 gaussian_noise_15[0][0]
==============================================================================================================
Total params: 372,771
Trainable params: 371,415
Non-trainable params: 1,356
model.summary(line_length=110)
______________________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==============================================================================================================
input_1 (InputLayer) (None, 368, 256, 4) 0
______________________________________________________________________________________________________________
gaussian_noise_1 (GaussianNoise) (None, 368, 256, 4) 0 input_1[0][0]
______________________________________________________________________________________________________________
conv2d_1 (Conv2D) (None, 368, 256, 32) 1184 gaussian_noise_1[0][0]
______________________________________________________________________________________________________________
batch_normalization_1 (BatchNormali (None, 368, 256, 32) 128 conv2d_1[0][0]
______________________________________________________________________________________________________________
gaussian_noise_2 (GaussianNoise) (None, 368, 256, 32) 0 batch_normalization_1[0][0]
______________________________________________________________________________________________________________
conv2d_2 (Conv2D) (None, 184, 128, 32) 9248 gaussian_noise_2[0][0]
______________________________________________________________________________________________________________
batch_normalization_2 (BatchNormali (None, 184, 128, 32) 128 conv2d_2[0][0]
______________________________________________________________________________________________________________
gaussian_noise_3 (GaussianNoise) (None, 184, 128, 32) 0 batch_normalization_2[0][0]
______________________________________________________________________________________________________________
conv2d_3 (Conv2D) (None, 184, 128, 64) 18496 gaussian_noise_3[0][0]
______________________________________________________________________________________________________________
batch_normalization_3 (BatchNormali (None, 184, 128, 64) 256 conv2d_3[0][0]
______________________________________________________________________________________________________________
gaussian_noise_4 (GaussianNoise) (None, 184, 128, 64) 0 batch_normalization_3[0][0]
______________________________________________________________________________________________________________
conv2d_4 (Conv2D) (None, 184, 128, 64) 36928 gaussian_noise_4[0][0]
______________________________________________________________________________________________________________
batch_normalization_4 (BatchNormali (None, 184, 128, 64) 256 conv2d_4[0][0]
______________________________________________________________________________________________________________
gaussian_noise_5 (GaussianNoise) (None, 184, 128, 64) 0 batch_normalization_4[0][0]
______________________________________________________________________________________________________________
up_sampling2d_1 (UpSampling2D) (None, 368, 256, 64) 0 gaussian_noise_5[0][0]
______________________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 368, 256, 96) 0 up_sampling2d_1[0][0]
batch_normalization_1[0][0]
______________________________________________________________________________________________________________
gaussian_noise_6 (GaussianNoise) (None, 368, 256, 96) 0 concatenate_1[0][0]
______________________________________________________________________________________________________________
conv2d_5 (Conv2D) (None, 368, 256, 64) 55360 gaussian_noise_6[0][0]
______________________________________________________________________________________________________________
batch_normalization_5 (BatchNormali (None, 368, 256, 64) 256 conv2d_5[0][0]
______________________________________________________________________________________________________________
gaussian_noise_7 (GaussianNoise) (None, 368, 256, 64) 0 batch_normalization_5[0][0]
______________________________________________________________________________________________________________
conv2d_6 (Conv2D) (None, 368, 256, 64) 36928 gaussian_noise_7[0][0]
______________________________________________________________________________________________________________
batch_normalization_6 (BatchNormali (None, 368, 256, 64) 256 conv2d_6[0][0]
______________________________________________________________________________________________________________
gaussian_noise_8 (GaussianNoise) (None, 368, 256, 64) 0 batch_normalization_6[0][0]
______________________________________________________________________________________________________________
conv2d_7 (Conv2D) (None, 368, 256, 64) 36928 gaussian_noise_8[0][0]
______________________________________________________________________________________________________________
batch_normalization_7 (BatchNormali (None, 368, 256, 64) 256 conv2d_7[0][0]
______________________________________________________________________________________________________________
gaussian_noise_9 (GaussianNoise) (None, 368, 256, 64) 0 batch_normalization_7[0][0]
______________________________________________________________________________________________________________
conv2d_8 (Conv2D) (None, 368, 256, 64) 36928 gaussian_noise_9[0][0]
______________________________________________________________________________________________________________
batch_normalization_8 (BatchNormali (None, 368, 256, 64) 256 conv2d_8[0][0]
______________________________________________________________________________________________________________
gaussian_noise_10 (GaussianNoise) (None, 368, 256, 64) 0 batch_normalization_8[0][0]
______________________________________________________________________________________________________________
conv2d_9 (Conv2D) (None, 368, 256, 64) 36928 gaussian_noise_10[0][0]
______________________________________________________________________________________________________________
batch_normalization_9 (BatchNormali (None, 368, 256, 64) 256 conv2d_9[0][0]
______________________________________________________________________________________________________________
gaussian_noise_11 (GaussianNoise) (None, 368, 256, 64) 0 batch_normalization_9[0][0]
______________________________________________________________________________________________________________
up_sampling2d_2 (UpSampling2D) (None, 736, 512, 64) 0 gaussian_noise_11[0][0]
______________________________________________________________________________________________________________
input_2 (InputLayer) (None, 736, 512, 3) 0
______________________________________________________________________________________________________________
concatenate_2 (Concatenate) (None, 736, 512, 67) 0 up_sampling2d_2[0][0]
input_2[0][0]
______________________________________________________________________________________________________________
gaussian_noise_12 (GaussianNoise) (None, 736, 512, 67) 0 concatenate_2[0][0]
______________________________________________________________________________________________________________
conv2d_10 (Conv2D) (None, 736, 512, 67) 40468 gaussian_noise_12[0][0]
______________________________________________________________________________________________________________
batch_normalization_10 (BatchNormal (None, 736, 512, 67) 268 conv2d_10[0][0]
______________________________________________________________________________________________________________
gaussian_noise_13 (GaussianNoise) (None, 736, 512, 67) 0 batch_normalization_10[0][0]
______________________________________________________________________________________________________________
conv2d_11 (Conv2D) (None, 736, 512, 67) 40468 gaussian_noise_13[0][0]
______________________________________________________________________________________________________________
batch_normalization_11 (BatchNormal (None, 736, 512, 67) 268 conv2d_11[0][0]
______________________________________________________________________________________________________________
gaussian_noise_14 (GaussianNoise) (None, 736, 512, 67) 0 batch_normalization_11[0][0]
______________________________________________________________________________________________________________
conv2d_12 (Conv2D) (None, 736, 512, 32) 19328 gaussian_noise_14[0][0]
______________________________________________________________________________________________________________
batch_normalization_12 (BatchNormal (None, 736, 512, 32) 128 conv2d_12[0][0]
______________________________________________________________________________________________________________
gaussian_noise_15 (GaussianNoise) (None, 736, 512, 32) 0 batch_normalization_12[0][0]
______________________________________________________________________________________________________________
conv2d_13 (Conv2D) (None, 736, 512, 3) 867 gaussian_noise_15[0][0]
==============================================================================================================
Total params: 372,771
Trainable params: 371,415
Non-trainable params: 1,356
___________________________
@FCOS我认为区别在于，你没有训练一个模型，而另一个模型是经过训练的 保存经过训练的模型时，它会保存
model1model2如果您有任何意见，请告诉我们。谢谢 372771个参数转换为大约1.4MB，因此我猜模型2的图形要小得多。也许看一下tensorboard中的两个图形？检查图形，它们是相同的。可能是因为我没有对新车型进行任何培训吗？这类信息会存储在模型保存文件中吗？谢谢！我没有意识到优化器配置数据会有多大。。。。
import tensorflow as tf
model1 = tf.keras.models.Sequential([
tf.keras.layers.Dense(128, activation='relu',input_shape=(None, None, 784,)),
tf.keras.layers.Dense(256, activation='relu'),
tf.keras.layers.Dense(512, activation='relu'),
tf.keras.layers.Dense(256, activation='relu'),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(10, activation='softmax')
])
model1.save('mymodel1.h5',overwrite=True,include_optimizer=True)
model1.summary()
Model: "sequential_10"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_20 (Dense) (None, None, None, 128) 100480
_________________________________________________________________
dense_21 (Dense) (None, None, None, 256) 33024
_________________________________________________________________
dense_22 (Dense) (None, None, None, 512) 131584
_________________________________________________________________
dense_23 (Dense) (None, None, None, 256) 131328
_________________________________________________________________
dense_24 (Dense) (None, None, None, 128) 32896
_________________________________________________________________
dense_25 (Dense) (None, None, None, 64) 8256
_________________________________________________________________
dense_26 (Dense) (None, None, None, 10) 650
=================================================================
Total params: 438,218
Trainable params: 438,218
Non-trainable params: 0
model2 = tf.keras.models.Sequential([
tf.keras.layers.Dense(128, activation='relu',input_shape=(300, 300, 784,)),
tf.keras.layers.Dense(256, activation='relu'),
tf.keras.layers.Dense(512, activation='relu'),
tf.keras.layers.Dense(256, activation='relu'),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(10, activation='softmax')
])
model2.save('mymodel2.h5',overwrite=True,include_optimizer=True)
model2.summary()
Model: "sequential_11"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_27 (Dense) (None, 300, 300, 128) 100480
_________________________________________________________________
dense_28 (Dense) (None, 300, 300, 256) 33024
_________________________________________________________________
dense_29 (Dense) (None, 300, 300, 512) 131584
_________________________________________________________________
dense_30 (Dense) (None, 300, 300, 256) 131328
_________________________________________________________________
dense_31 (Dense) (None, 300, 300, 128) 32896
_________________________________________________________________
dense_32 (Dense) (None, 300, 300, 64) 8256
_________________________________________________________________
dense_33 (Dense) (None, 300, 300, 10) 650
=================================================================
Total params: 438,218
Trainable params: 438,218
Non-trainable params: 0
_________________________________________________________________