CODEWITHSUNDEEP
pythontensorflowkerasneural-networkimage-classification
Daniel R
Daniel R

Reputation: 21

Neural Network optimization for image classification in keras/tensorflow

I am writing a program for clasifying images into two categories: "Wires" and "non-Wires". I have hand-labeled around 5000 microscope images, examples:

non-wire

enter image description here

wire

enter image description here

The neural network I am using is adapted from "Deep Learning with Python", chapter about convolutional networks (I don't think convolutional networks are neccesary here because there are no obvious hierarchies; Dense networks should be more suitable):

model = models.Sequential()
model.add(layers.Dense(32, activation='relu',input_shape=(200,200,3)))
model.add(layers.MaxPooling2D((2,2)))
model.add(layers.Dense(32, activation='relu'))
model.add(layers.MaxPooling2D((2,2)))
model.add(layers.Flatten())
model.add(layers.Dense(256, activation='relu'))
model.add(layers.Dense(2, activation='softmax'))

However, test accuracy after 10 epochs of training does not go over 92% when playing around with the paramters of the network. Training images contain about 1/3 wires, 2/3 non-wires. My question: Do you see any obvious mistakes in this neural network design that inhibits accuracy, or do you think I am limited by the image quality? I have about 4000 train and 1000 test images.

Upvotes: 0

Views: 278

Answers (1)

Gerry P
Gerry P

Reputation: 8102

You might get some improvement by trying to handle the class imbalance using a weights dictionary. If the label of non wire is 0 and the label for wire is 1 then the weight dictionary would be

weight_dict= { 0:.5, 1:1}

in model.fit set

 class_weight=weight_dict .

Without seeing the results of training (training loss and validation loss) can't tell what else to do. If you are over fitting try adding some dropout layers. Also recommend you try using an adjustable learning using the keras callback ReduceLROnPlateau, and early stopping using the keras callback EarlyStopping. Documentation is here. Set each callback to monitor validation loss. My suggested code is shown below:

reduce_lr=tf.keras.callbacks.ReduceLROnPlateau(
          monitor="val_loss",factor=0.5, patience=2, verbose=1)
e_stop=tf.keras.callbacks.EarlyStopping( monitor="val_loss", patience=5, 
          verbose=0,  restore_best_weights=True)
callbacks=[reduce_lr, e_stop]

In model.fit include

callbacks=callbacks

If you want to give a convolutional network a try I recommend transfer learning using the Mobilenetmodel. Documentation for that is here.. My recommend code for that is below:

base_model=tf.keras.applications.mobilenet.MobileNet( include_top=False, 
          input_shape=(200,200,3) pooling='max', weights='imagenet',dropout=.4) 
x=base_model.output
x=keras.layers.BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001 )(x)
x = Dense(1024, activation='relu')(x)
x=Dropout(rate=.3, seed=123)(x) 
output=Dense(2, activation='softmax')(x)
model=Model(inputs=base_model.input, outputs=output)
model.compile(Adamax(lr=.001),loss='categorical_crossentropy',metrics= 
              ['accuracy'] )

In model.fit include the callbacks as shown above.

Upvotes: 1

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