Regularizing the image recognition model.

In Deep Learning for Python, Fran├žois Chollet provides a “universal workflow of machine learning”. (Chapter 4, page 114.) I have been using his steps to seek the best performing image recognition model. I tried iterations of various models with different numbers of layers, filters and dropouts. An example of a model that did not provide a satisfactory level of accuracy is below.

def createModel5fail2():

    #tried kernel_size=(5,5), 

    from keras import models
    model = models.Sequential()    

    model.add(Conv2D(filters=32, 
               kernel_size=(5,5), 
               strides=(1,1),
               padding='same',
               input_shape=(image_width, image_height,NB_CHANNELS),
               data_format='channels_last'))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2,2),
                     strides=2))

    model.add(Conv2D(filters=64,
               kernel_size=(5,5),
               strides=(1,1),
               padding='valid'))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2,2),
                     strides=2))
    
    model.add(Flatten())        
    model.add(Dense(128))
    model.add(Activation('relu'))

    model.add(Dropout(0.25))
    
    #number of classes
    # 1,0,0 E-II
    # 0,1,0 G-VI
    # 0,0,1 G-VI
    model.add(Dense(3, activation='softmax'))

    return model 

In order to be more methodical and record my results I added a spreadsheet of model parameters (see models tab). These are the parameters:

  • model_number – the identification number
  • batch_size – the size of the batch. 8 or 16
  • filters1 – the number of filters for layer 1.
model.add(Conv2D(filters=filters1, 
  • dropout1 – Dropout (if greater than 0)

if(dropout1>0):
model.add(Dropout(dropout1))

  • filters2 – the number of filters for layer 2.
  • dropout2 – dropout for layer 2.
  • filters3 – the number of filters for layer 3.
  • dropout3 – dropout for layer 3.
  • loss – the result of running the model.
  • accuracy – (as above.)

The code to create the spreadsheet of parameters is here. (It’s just nested loops.) Below is the code to create a model from parameters fed from the spreadsheet. In the course of writing up this post, I found 2 bugs in the code below that are now corrected. Because of the bugs I need to re-run my results.

def createModelFromSpreadsheet():

    from keras import models
    model = models.Sequential()
    

    model.add(Conv2D(filters=filters1, 
               kernel_size=(2,2), 
               strides=(1,1),
               padding='same',
               input_shape=(image_width, image_height,NB_CHANNELS),
               data_format='channels_last'))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2,2),
                     strides=2))
    if(dropout1>0):
        model.add(Dropout(dropout1))
    
    model.add(Conv2D(filters=filters2,
               kernel_size=(2,2),
               strides=(1,1),
               padding='valid'))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2,2),
                     strides=2))

    if(dropout2>0):
        model.add(Dropout(dropout2))

    if(filters3>0): 
        model.add(Conv2D(filters=filters3,
               kernel_size=(2,2),
               strides=(1,1),
               padding='valid'))
        model.add(Activation('relu'))
        model.add(MaxPooling2D(pool_size=(2,2),
                     strides=2))

        if(dropout3>0):
            model.add(Dropout(dropout3))

    
    model.add(Flatten())        
    model.add(Dense(512))
    model.add(Activation('relu'))
    model.add(Dropout(0.25))
    
    #number of classes
    # 1,0,0 E-II
    # 0,1,0 G-VI
    # 0,0,1 G-VI
    model.add(Dense(3, activation='softmax'))

    return model
  

Below is the code to loop through each row of the model spreadsheet, create a model from the parameters, fit it and record the result.

number_of_models_to_run = 40
for number_of_models_to_run_count in range (0,number_of_models_to_run):
    model_row = int(worksheet_config.cell(1, 2).value)

    BATCH_SIZE = int(worksheet_models.cell(model_row, 2).value,0) #, 'batch_size')
    filters1 = int(worksheet_models.cell(model_row, 3).value,0) #, 'filters1')
    dropout1 = float(worksheet_models.cell(model_row, 4).value) #, 'dropout1')
    filters2 = int(worksheet_models.cell(model_row, 5).value,0) #, 'filters2')
    dropout2 = float(worksheet_models.cell(model_row, 6).value) #, 'dropout2')
    filters3 = int(worksheet_models.cell(model_row, 7).value,0) #, 'filters3')
    dropout3 = float(worksheet_models.cell(model_row, 8).value) #, 'dropout3')

    print(str(model_row)+" "+str(BATCH_SIZE)+" "+str(filters1)+" "+str(dropout1)+" "+str(filters2)+" "+str(dropout2)+" "+str(filters3)+" "+str(dropout3))
    # NB_CHANNELS = # 3 for RGB images or 1 for grayscale images
    NB_CHANNELS =  1
    NB_TRAIN_IMG = 111
    # NB_VALID_IMG = # Replace with the total number validation images  
    NB_VALID_IMG = 54


    #*************
    #* Change model
    #*************
    model2 = createModelFromSpreadsheet()
    model2.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
    model2.summary()

    epochs = 100

    # Fit the model on the batches generated by datagen.flow().
    history2 = model2.fit_generator(datagen.flow(tr_img_data , tr_lbl_data, batch_size=BATCH_SIZE),
                                  #steps_per_epoch=int(np.ceil(tr_img_data .shape[0] / float(batch_size))),
                                  steps_per_epoch=NB_TRAIN_IMG//BATCH_SIZE,
                                  epochs=epochs,
                                  validation_data=(val_img_data, val_lbl_data),
                                  validation_steps=NB_VALID_IMG//BATCH_SIZE,
                                  shuffle=True,
                                  workers=4)

    evaluation = model2.evaluate(tst_img_data, tst_lbl_data)
    print(evaluation)
    print(evaluation[0])
    #record results
    worksheet_models.update_cell(model_row, 10, evaluation[0])
    worksheet_models.update_cell(model_row, 11, evaluation[1])
    worksheet_config.update_cell(1, 2, str(model_row+1))
    if(evaluation[1]>0.75):
        number_of_models_to_run_count = number_of_models_to_run
        print("Good Model - stopped")

In the course of running these models, I had a model that provided 77% image recognition accuracy when tested and so I saved the weights. Due to the bugs I found I am re-running my results now to see if I can reproduce the model and find a better one.

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