My application is accident-avoidance car systems using Machine Learning (Convolutional Neural Networks). My images are 200x100 JPG images and the output is an array of 4 elements: the car would move left, right, stop or move forward. So the output will let one element be 1 (according to the correct action that should be taken) and the 3 other elements will be 0.

I want to train my machine now in order to help it input any image and decide on the action independently. Here's my code:

from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.optimizers import SGDimport numpy as npmodel = Sequential()model.add(Convolution2D(16, 1, 1, border_mode='valid', dim_ordering='tf', input_shape=(200, 150, 1)))
model.add(Activation('relu'))
model.add(Convolution2D(16, 1, 1))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25)) #Cannot take float valuesmodel.add(Convolution2D(32, 1, 1, border_mode='valid'))
model.add(Activation('relu'))
model.add(Convolution2D(32, 1, 1))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))model.add(Flatten())
# Note: Keras does automatic shape inference.
model.add(Dense(256))
model.add(Activation('relu'))
model.add(Dropout(0.5))model.add(Dense(10))
model.add(Activation('softmax'))model.fit(X_train, Y_train, batch_size=32, nb_epoch=1)

How can I input my images (I have them on my PC)? And how can I specify the Y-train?

This Keras blog post, Building powerful image classification models using very little data, is an excellent tutorial for training a model on images stored in directories. It also introduces the ImageDataGenerator class, which has the member function flow_from_directory referenced in @isaac-moore's answer. flow from directory can be used train on images, where the directory structure is used to deduce the value of Y_train.

The three python scripts that accompany the tutorial blog post can be found at the links below:

1. classifier_from_little_data_script_1.py
2. classifier_from_little_data_script_2.py
3. classifier_from_little_data_script_3.py

(Of course, these links are in the blog post itself, but the links are not centrally located.) Note that scripts 2 and 3 build on the output of the previous. Also, note that additional files will need to be downloaded from Kaggle and Github.