I want to run the code
from utils import INPUT_SHAPE, batch_generator
First, it was giving the error
<No module named utils>
When I solved that, now running that code line is giving this error:
ImportError: cannot import name 'INPUT_SHAPE' from 'utils'
(C:\Users\Lenovo\anaconda3\lib\site-packages\utils\__init__.py)
The whole code I'm trying to implement is:
import pandas as pd # data analysis toolkit - create, read, update, delete datasets
import numpy as np #matrix math
from sklearn.model_selection import train_test_split #to split out training and testing data
from keras.models import Sequential
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint
from keras.layers import Lambda, Conv2D, MaxPooling2D, Dropout, Dense, Flatten
#helper class to define input shape and generate training images given image paths & steering angles
from utils import INPUT_SHAPE, batch_generator
import argparse
import os
#for debugging, allows for reproducible (deterministic) results
np.random.seed(0)
def load_data(args):
"""
Load training data and split it into training and validation set
"""
#reads CSV file into a single dataframe variable
data_df = pd.read_csv(os.path.join(os.getcwd(), args.data_dir, 'driving_log.csv'), names=['center', 'left', 'right', 'steering', 'throttle', 'reverse', 'speed'])
#yay dataframes, we can select rows and columns by their names
#we'll store the camera images as our input data
X = data_df[['center', 'left', 'right']].values
#and our steering commands as our output data
y = data_df['steering'].values
#now we can split the data into a training (80), testing(20), and validation set
#thanks scikit learn
X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=args.test_size, random_state=0)
return X_train, X_valid, y_train, y_valid
def build_model(args):
"""
NVIDIA model used
Image normalization to avoid saturation and make gradients work better.
Convolution: 5x5, filter: 24, strides: 2x2, activation: ELU
Convolution: 5x5, filter: 36, strides: 2x2, activation: ELU
Convolution: 5x5, filter: 48, strides: 2x2, activation: ELU
Convolution: 3x3, filter: 64, strides: 1x1, activation: ELU
Convolution: 3x3, filter: 64, strides: 1x1, activation: ELU
Drop out (0.5)
Fully connected: neurons: 100, activation: ELU
Fully connected: neurons: 50, activation: ELU
Fully connected: neurons: 10, activation: ELU
Fully connected: neurons: 1 (output)
# the convolution layers are meant to handle feature engineering
the fully connected layer for predicting the steering angle.
dropout avoids overfitting
ELU(Exponential linear unit) function takes care of the Vanishing gradient problem.
"""
model = Sequential()
model.add(Lambda(lambda x: x/127.5-1.0, input_shape=INPUT_SHAPE))
model.add(Conv2D(24, 5, 5, activation='elu', subsample=(2, 2)))
model.add(Conv2D(36, 5, 5, activation='elu', subsample=(2, 2)))
model.add(Conv2D(48, 5, 5, activation='elu', subsample=(2, 2)))
model.add(Conv2D(64, 3, 3, activation='elu'))
model.add(Conv2D(64, 3, 3, activation='elu'))
model.add(Dropout(args.keep_prob))
model.add(Flatten())
model.add(Dense(100, activation='elu'))
model.add(Dense(50, activation='elu'))
model.add(Dense(10, activation='elu'))
model.add(Dense(1))
model.summary()
return model
def train_model(model, args, X_train, X_valid, y_train, y_valid):
"""
Train the model
"""
#Saves the model after every epoch.
#quantity to monitor, verbosity i.e logging mode (0 or 1),
#if save_best_only is true the latest best model according to the quantity monitored will not be overwritten.
#mode: one of {auto, min, max}. If save_best_only=True, the decision to overwrite the current save file is
# made based on either the maximization or the minimization of the monitored quantity. For val_acc,
#this should be max, for val_loss this should be min, etc. In auto mode, the direction is automatically
# inferred from the name of the monitored quantity.
checkpoint = ModelCheckpoint('model-{epoch:03d}.h5',
monitor='val_loss',
verbose=0,
save_best_only=args.save_best_only,
mode='auto')
#calculate the difference between expected steering angle and actual steering angle
#square the difference
#add up all those differences for as many data points as we have
#divide by the number of them
#that value is our mean squared error! this is what we want to minimize via
#gradient descent
model.compile(loss='mean_squared_error', optimizer=Adam(lr=args.learning_rate))
#Fits the model on data generated batch-by-batch by a Python generator.
#The generator is run in parallel to the model, for efficiency.
#For instance, this allows you to do real-time data augmentation on images on CPU in
#parallel to training your model on GPU.
#so we reshape our data into their appropriate batches and train our model simulatenously
model.fit_generator(batch_generator(args.data_dir, X_train, y_train, args.batch_size, True),
args.samples_per_epoch,
args.nb_epoch,
max_q_size=1,
validation_data=batch_generator(args.data_dir, X_valid, y_valid, args.batch_size, False),
nb_val_samples=len(X_valid),
callbacks=[checkpoint],
verbose=1)
#for command line args
def s2b(s):
"""
Converts a string to boolean value
"""
s = s.lower()
return s == 'true' or s == 'yes' or s == 'y' or s == '1'
def main():
"""
Load train/validation data set and train the model
"""
parser = argparse.ArgumentParser(description='Behavioral Cloning Training Program')
parser.add_argument('-d', help='data directory', dest='data_dir', type=str, default='data')
parser.add_argument('-t', help='test size fraction', dest='test_size', type=float, default=0.2)
parser.add_argument('-k', help='drop out probability', dest='keep_prob', type=float, default=0.5)
parser.add_argument('-n', help='number of epochs', dest='nb_epoch', type=int, default=10)
parser.add_argument('-s', help='samples per epoch', dest='samples_per_epoch', type=int, default=20000)
parser.add_argument('-b', help='batch size', dest='batch_size', type=int, default=40)
parser.add_argument('-o', help='save best models only', dest='save_best_only', type=s2b, default='true')
parser.add_argument('-l', help='learning rate', dest='learning_rate', type=float, default=1.0e-4)
args = parser.parse_args()
#print parameters
print('-' * 30)
print('Parameters')
print('-' * 30)
for key, value in vars(args).items():
print('{:<20} := {}'.format(key, value))
print('-' * 30)
#load data
data = load_data(args)
#build model
model = build_model(args)
#train model on data, it saves as model.h5
train_model(model, args, *data)
if __name__ == '__main__':
main()
EDIT
I checked for utils.py files in anaconda3 folder and found 9 of them. I also checked for INPUT_SHAPE in each of the python file but couldn't find it any one of them. 9 files of utils.py
[enter image description here][2]
I found your code in project How_to_simulate_a_self_driving_car in file model.py. This project has also file utils.py with INPUT_SHAPE.
It looks like you copy file model.py but you forgot to copy file utils.py.
You should have both file in the same folder - ie. C:\Users\Lenovo - and start python model.py from this folder C:\Users\Lenovo
So copy utils.py from project How_to_simulate_a_self_driving_car to your project.
But it would be better to copy all files from project How_to_simulate_a_self_driving_car because it may need other files from this project.
