alexeygrigorev / clothing-dataset Goto Github PK

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from tensorflow import keras
############################# Creating Model #############################
base_model = keras.applications.Xception(
    weights='imagenet',  # Load weights pre-trained on ImageNet.
    input_shape=(150, 150, 3),
    include_top=False)  # Do not include the ImageNet classifier at the top.

image_size=(150,150)

inputs = keras.Input(shape= image_size + (3,))
## Set the base model trainable false to freeze training on the resnet model
# x = base_model(inputs, training = False)
base_model.training = False
model = keras.Sequential()
model.add(base_model)
model.add(keras.layers.Flatten())
## Dense layer with 20 nodes ( we had 20 classes )
model.add(keras.layers.Dense(20, activation='softmax'))

############################# Getting dataset batches #############################
train_generator = tf.keras.preprocessing.image.ImageDataGenerator(
    rescale=1./255,
    validation_split=0.1)
valid_generator = tf.keras.preprocessing.image.ImageDataGenerator(
    rescale=1./255,
    validation_split=0.1)
### Note here we classified folders so we're using flow_from directory method
train_dataset = train_generator.flow_from_directory('dataset',
                                                    target_size=image_size, 
                                                    batch_size=12,
                                                    subset='training',
                                                    class_mode='categorical',
                                                    shuffle=True,
                                                    seed=123)
validation_dataset = valid_generator.flow_from_directory('dataset',
                                                          target_size=image_size,
                                                          batch_size=12,
                                                          shuffle=True,
                                                          subset='validation',
                                                          interpolation='bilinear',
                                                          class_mode='categorical',
                                                          seed=123)

############################# Calculating classes weight #############################
cloth_df = pd.read_csv('images.csv')

## Get the each class values count 
class_values_count = cloth_df.label.value_counts()

## Sort them by index ( indexes are label names as 'T-Shirt', 'Blouse',etc )
class_values_count = class_values_count.sort_index()


## Because our classes don't have similar count
## we would define a function to calculate wights for us
def calculate_weights(class_values_count):
    ## get the max class count
    max_count = class_values_count.max()
    ## Create an empty dictionary
    weights = {}
    ## indexes are used to add to dictionary
    index = 0
    for counts in class_values_count:
        ## Calculate the weights
        ## max_count is 1011 for T-Shirt class
        weights[index] = max_count/counts
        index+=1
    
    return weights
        
class_weight = calculate_weights(class_values_count)
## Also the scikit learn function for weighting was tried ( It is as below )
## sklearn.utils.class_weight.compute_class_weight('balanced', np.unique(values), values)

############################# Compiling and Training the model #############################
model.compile(optimizer=keras.optimizers.Adam(),
            loss="categorical_crossentropy",
            metrics=["categorical_accuracy"],)


epochs = 40

callbacks = [
    tf.keras.callbacks.ModelCheckpoint("transfer_learning_save_at_{epoch}.h5"),
]

## Here I tried both with weight and without weight training
## The last time I tried without weight training, So class_weight is commented
history = model.fit(
    train_dataset, epochs=epochs, callbacks=callbacks,
    validation_data=validation_dataset
#     ,class_weight=class_weight
)