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import numpy as np
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1'
import tensorflow as tf
import pandas as pd
tf.python.control_flow_ops = tf
from keras import models, optimizers, backend
from keras.layers import core, convolutional, pooling
from sklearn import model_selection
from data import generate_samples, preprocess
from weights_logger_callback import WeightsLogger
local_project_path = '/'
local_data_path = os.path.join(local_project_path, 'data')
if __name__ == '__main__':
# Read the data
df = pd.io.parsers.read_csv(os.path.join(local_data_path, 'driving_log.csv'))
# Split data into training and validation sets
df_train, df_valid = model_selection.train_test_split(df, test_size=.2)
# Model architecture
model = models.Sequential()
model.add(convolutional.Convolution2D(16, 3, 3, input_shape=(32, 128, 3), activation='relu'))
model.add(pooling.MaxPooling2D(pool_size=(2, 2)))
model.add(convolutional.Convolution2D(32, 3, 3, activation='relu'))
model.add(pooling.MaxPooling2D(pool_size=(2, 2)))
model.add(convolutional.Convolution2D(64, 3, 3, activation='relu'))
model.add(pooling.MaxPooling2D(pool_size=(2, 2)))
model.add(core.Flatten())
model.add(core.Dense(500, activation='relu'))
model.add(core.Dropout(.5))
model.add(core.Dense(100, activation='relu'))
model.add(core.Dropout(.25))
model.add(core.Dense(20, activation='relu'))
model.add(core.Dense(1))
model.compile(optimizer=optimizers.Adam(lr=1e-04), loss='mean_squared_error')
history = model.fit_generator(
generate_samples(df_train, local_data_path),
samples_per_epoch=df_train.shape[0],
nb_epoch=30,
validation_data=generate_samples(df_valid, local_data_path, augment=False),
callbacks=[WeightsLogger(root_path=local_project_path)]
nb_val_samples=df_valid.shape[0],
)
with open(os.path.join(local_project_path, 'model.json'), 'w') as file:
file.write(model.to_json())
backend.clear_session()