Keras example image regression, extract texture height param

brix.py

# -*- coding: utf-8 -*-
import numpy as np
import os
import cv2
import pandas as pd

from sklearn.cross_validation import train_test_split
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution2D
from keras.optimizers import RMSprop, Adam, Adadelta

image_size = 50

def load_train():
    X_train = []
    y_train = []
    heights = pd.read_csv('heights.csv')
    print('Read train images')
    for index, row in heights.iterrows():
        image_path = os.path.join('images', 'train', str(int(row['img'])) + '.png')
        img = cv2.resize(cv2.imread(image_path, cv2.CV_LOAD_IMAGE_COLOR), (image_size, image_size) ).astype(np.float32)
        img = img.transpose((2,0,1))
        X_train.append(img)
        y_train.append( [ row['height'] ] )
    return X_train, y_train

def read_and_normalize_train_data():
    train_data, train_target = load_train()
    train_data = np.array(train_data, dtype=np.float32)
    train_target = np.array(train_target, dtype=np.float32)
    m = train_data.mean()
    s = train_data.std()

    print ('Train mean, sd:', m, s )
    train_data -= m
    train_data /= s
    print('Train shape:', train_data.shape)
    print(train_data.shape[0], 'train samples')
    return train_data, train_target


def create_model():
    nb_filters = 8
    nb_conv = 5

    model = Sequential()
    model.add(Convolution2D(nb_filters, nb_conv, nb_conv,
                            border_mode='valid',
                            input_shape=(3, image_size, image_size) ) )
    model.add(Activation('relu'))

    model.add(Convolution2D(nb_filters, nb_conv, nb_conv))
    model.add(Activation('relu'))

    model.add(Convolution2D(nb_filters, nb_conv, nb_conv))
    model.add(Activation('relu'))

    model.add(Convolution2D(nb_filters, nb_conv, nb_conv))
    model.add(Activation('relu'))
    model.add(Dropout(0.25))

    model.add(Convolution2D(nb_filters*2, nb_conv, nb_conv))
    model.add(Activation('relu'))

    model.add(Convolution2D(nb_filters*2, nb_conv, nb_conv))
    model.add(Activation('relu'))

    model.add(Convolution2D(nb_filters*2, nb_conv, nb_conv))
    model.add(Activation('relu'))

    model.add(Convolution2D(nb_filters*2, nb_conv, nb_conv))
    model.add(Activation('relu'))
    model.add(Dropout(0.5))

    model.add(Flatten())
    model.add(Dense(256))
    model.add(Activation('relu'))
    model.add(Dropout(0.5))

    model.add(Dense(128))
    model.add(Activation('relu'))
    model.add(Dropout(0.5))

    model.add(Dense(1))
    model.add(Activation('linear'))

    model.compile(loss='mean_squared_error', optimizer=Adadelta())
    return model

def train_model(batch_size = 50, nb_epoch = 20):
    num_samples = 1999
    cv_size = 499

    train_data, train_target = read_and_normalize_train_data()
    train_data = train_data[0:num_samples,:,:,:]
    train_target = train_target[0:num_samples]

    X_train, X_valid, y_train, y_valid = train_test_split(train_data, train_target, test_size=cv_size, random_state=56741)

    model = create_model()
    model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=nb_epoch, verbose=1, validation_data=(X_valid, y_valid) )

    predictions_valid = model.predict(X_valid, batch_size=50, verbose=1)
    compare = pd.DataFrame(data={'original':y_valid.reshape((cv_size,)),
             'prediction':predictions_valid.reshape((cv_size,))})
    compare.to_csv('compare.csv')

    return model

train_model(nb_epoch = 50)