remove only last(dense) layer of an already trained model, keeping all the weights of the model inta-CodePudding

I want to remove only the last dense layer from an already saved model in .h5 file and add a new dense layer.

Information about the saved model:

I used transfer learning on the EfficientNet B0 model and added a dropout with 2 dense layers. The last dense layer had 3 nodes equal to my number of classes, as shown below:

inputs = tf.keras.layers.Input(shape=(IMAGE_HEIGHT, IMAGE_WIDTH, 3))
x = img_augmentation(inputs)
model = tf.keras.applications.EfficientNetB0(include_top=False, input_tensor=x, weights="imagenet")
# Freeze the pretrained weights
model.trainable = False
# Rebuild top
x = tf.keras.layers.GlobalAveragePooling2D(name="avg_pool")(model.output)
x = tf.keras.layers.BatchNormalization()(x)

x = tf.keras.layers.Dropout(0.3)(x)
x = tf.keras.layers.Dense(5, activation=tf.nn.relu)(x)

outputs = tf.keras.layers.Dense(len(class_names), activation="softmax", name="pred")(x)

After training, I saved my model as my_h5_model.h5

Main Task: I want to use the saved model architecture with its weights and replace only the last dense layer with 4 nodes dense layer.

I tried many things as suggested by the StackOverflow community as:

Iterate over all the layers except the last layer and add them to a separate already defined sequential model

new_model = Sequential()
for layer in (model.layers[:-1]):
    new_model.add(layer)

But it gives an error which state:

ValueError: Exception encountered when calling layer "block1a_se_excite" (type Multiply).

A merge layer should be called on a list of inputs. Received: inputs=Tensor("Placeholder:0", shape=(None, 1, 1, 32), dtype=float32) (not a list of tensors)

Call arguments received:

• inputs=tf.Tensor(shape=(None, 1, 1, 32), dtype=float32)

I also tried the functional approach as:

input_layer = model.input
for layer in (model.layers[:-1]):
    x = layer(input_layer)

which throws an as mention below:

ValueError: Exception encountered when calling layer "stem_bn" (type BatchNormalization).

Dimensions must be equal, but are 3 and 32 for '{{node stem_bn/FusedBatchNormV3}} = FusedBatchNormV3[T=DT_FLOAT, U=DT_FLOAT, data_format="NHWC", epsilon=0.001, exponential_avg_factor=1, is_training=false](Placeholder, stem_bn/ReadVariableOp, stem_bn/ReadVariableOp_1, stem_bn/FusedBatchNormV3/ReadVariableOp, stem_bn/FusedBatchNormV3/ReadVariableOp_1)' with input shapes: [?,224,224,3], [32], [32], [32], [32].

Call arguments received:

• inputs=tf.Tensor(shape=(None, 224, 224, 3), dtype=float32)

• training=False

Lastly, I did something that came to my mind

inputs = tf.keras.layers.Input(shape=(IMAGE_HEIGHT, IMAGE_WIDTH, 3))
x = img_augmentation(inputs)
x = model.layers[:-1](x)
x = keras.layers.Dense(5, name="compress_1")(x)

which simply gave an error as:

'list' object is not callable

CodePudding user response：

Have you tried switching between import keras and tensorflow.keras in your import? This has worked in other issues.

CodePudding user response：

I did some more experiments and was able to remove the last layer and add the new dense layer

# imported a pretained saved model  
from tensorflow import keras
import tensorflow as tf

model = keras.models.load_model('/content/my_h5_model.h5')

# selected all layers except last one
x= model.layers[-2].output 
outputs = tf.keras.layers.Dense(4, activation="softmax", name="predictions")(x)
model = tf.keras.Model(inputs = model.input, outputs = outputs)
model.summary()

In the saved model, I had 3 nodes on dense layers, but in the current model, I added 4 layers. The last layer summary is shown below:

 dropout_3 (Dropout)            (None, 1280)         0           ['batch_normalization_4[0][0]']  
                                                                                                  
 dense_3 (Dense)                (None, 5)            6405        ['dropout_3[0][0]']              
                                                                                                  
 predictions (Dense)            (None, 4)            24          ['dense_3[0][0]']                
                                                                                                  
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