behavior-cloning-steering-control/model.py at master · sumitbinnani/behavior-cloning-steering-control · GitHub

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from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten, Convolution2D, Input, Lambda, SpatialDropout2D
from keras.optimizers import Adam
import tensorflow as tf
import pandas as pd
import os

from utils import process_img_from_path
from utils import training_image_generator
from utils import get_images
from utils import randomly_drop_low_steering_data


DATA_PATH = "../../data"
LABEL_PATH = os.path.join(DATA_PATH, "driving_log.csv")
BATCH_SIZE = 64
EPOCHS = 50


def model_nvidia(input_shape):
    """Model based on the following paper by Nvidia
    http://images.nvidia.com/content/tegra/automotive/images/2016/solutions/pdf/end-to-end-dl-using-px.pdf
    """

    def resize_images(img):
        """Returns resized image
        Cannot be directly used in lambda function
        as tf is not understood by keras
        """
        import tensorflow as tf
        return tf.image.resize_images(img, (66, 200))

    model = Sequential()
    model.add(Lambda(resize_images, input_shape=input_shape))
    model.add(Lambda(lambda x: x/255.-0.5))
    model.add(Convolution2D(24, 5, 5, border_mode="same", subsample=(2,2), activation="elu"))
    model.add(SpatialDropout2D(0.2))
    model.add(Convolution2D(36, 5, 5, border_mode="same", subsample=(2,2), activation="elu"))
    model.add(SpatialDropout2D(0.2))
    model.add(Convolution2D(48, 5, 5, border_mode="valid", subsample=(2,2), activation="elu"))
    model.add(SpatialDropout2D(0.2))
    model.add(Convolution2D(64, 3, 3, border_mode="valid", activation="elu"))
    model.add(SpatialDropout2D(0.2))
    model.add(Convolution2D(64, 3, 3, border_mode="valid", activation="elu"))
    model.add(SpatialDropout2D(0.2))

    model.add(Flatten())
    model.add(Dropout(0.5))
    model.add(Dense(100, activation="elu"))
    model.add(Dense(50, activation="elu"))
    model.add(Dense(10, activation="elu"))
    model.add(Dropout(0.5))
    model.add(Dense(1))

    model.compile(optimizer=Adam(lr=0.001), loss='mse')
    return model


## Load CSV File generated by simulator
csv_df = pd.read_csv(LABEL_PATH, index_col=False)
csv_df.columns = ['center', 'left', 'right', 'steer', 'throttle', 'brake', 'speed']

## Randomly Shuffle
csv_df = csv_df.sample(n=len(csv_df))

## Training and Validation Data
training_count = int(0.8 * len(csv_df))
training_data = csv_df[:training_count].reset_index()
validation_data = csv_df[training_count:].reset_index()

## Reducing low steering angle data to remove bias
training_data = randomly_drop_low_steering_data(training_data)

## Opening a random image to obatin input size for model
img = process_img_from_path(os.path.join(DATA_PATH, training_data['center'].iloc[909].strip()))

## Creating a model
model = model_nvidia(img.shape)
samples_per_epoch = int(len(training_data) / BATCH_SIZE) * BATCH_SIZE
nb_val_samples = len(validation_data)

values = model.fit_generator(training_image_generator(training_data, BATCH_SIZE, DATA_PATH), samples_per_epoch=samples_per_epoch, nb_epoch=EPOCHS, validation_data=get_images(validation_data, DATA_PATH), nb_val_samples=len(validation_data))

model.save('model.h5')