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We will freeze the bottom N layers # and train the remaining top layers. ) # at this point, the top layers are well trained and we can start fine-tuning # convolutional layers from inception V3. compile ( optimizer = 'rmsprop', loss = 'categorical_crossentropy' ) # train the model on the new data for a few epochs model. trainable = False # compile the model (should be done *after* setting layers to non-trainable) model. freeze all convolutional InceptionV3 layers for layer in base_model. input, outputs = predictions ) # first: train only the top layers (which were randomly initialized) # i.e. output x = GlobalAveragePooling2D ()( x ) # let's add a fully-connected layer x = Dense ( 1024, activation = 'relu' )( x ) # and a logistic layer - let's say we have 200 classes predictions = Dense ( 200, activation = 'softmax' )( x ) # this is the model we will train model = Model ( inputs = base_model.

Usage examples for image classification models Classify ImageNet classes with ResNet50įrom _v3 import InceptionV3 from import image from import Model from import Dense, GlobalAveragePooling2D # create the base pre-trained model base_model = InceptionV3 ( weights = 'imagenet', include_top = False ) # add a global spatial average pooling layer x = base_model.

CPU: AMD EPYC Processor (with IBPB) (92 core)ĭepth counts the number of layers with parameters.

Time per inference step is the average of 30 batches and 10 repetitions. This includes activation layers, batch normalization layers etc. The top-1 and top-5 accuracy refers to the model's performance on the ImageNet validation dataset.ĭepth refers to the topological depth of the network.

Then any model loaded from this repository will get built according to the TensorFlow data format convention, 'Height-Width-Depth'. Upon instantiation, the models will be built according to the image data format set in your Keras configuration file at ~/.keras/keras.json.įor instance, if you have set image_data_format=channels_last,

Weights are downloaded automatically when instantiating a model. These models can be used for prediction, feature extraction, and fine-tuning.

Keras Applications are deep learning models that are made available alongside pre-trained weights.

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