train.py

# Importing the Keras libraries and packages
# Import of keras model and hidden layers for our convolutional network
from keras.models import Sequential
from keras.layers.convolutional import Conv2D, MaxPooling2D
from keras.layers import Dense, Flatten


# Step 1 - Building the CNN

# Initializing the CNN
model = Sequential()

# First convolution layer and pooling
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(120, 120, 1)))
model.add(MaxPooling2D((2, 2)))

# Second convolution layer and pooling
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))

# Third convolution layer
model.add(Conv2D(128, (3, 3), activation='relu'))

# input_shape is going to be the pooled feature maps from the previous convolution layer
model.add(MaxPooling2D((2, 2)))
# Flattening the layers
model.add(Flatten())

# Adding a fully connected layer
model.add(Dense(256, activation='relu'))
model.add(Dense(7, activation='softmax'))
# Compiling the CNN
model.compile(optimizer='adam', # Optimization routine, which tells the computer how to adjust the parameter values to minimize the loss function.
              loss='categorical_crossentropy', # Loss function, which tells us how bad our predictions are.
              metrics=['accuracy']) # List of metrics to be evaluated by the model during training and testing. # categorical_crossentropy for more than 2


# Step 2 - Preparing the train/test data and training the model

# Code copied from - https://keras.io/preprocessing/image/
from keras.preprocessing.image import ImageDataGenerator


train_datagen = ImageDataGenerator(
        rescale=1./255,
        shear_range=0.2,
        zoom_range=0.2,
        horizontal_flip= False)

test_datagen = ImageDataGenerator(rescale=1./255)

training_set = train_datagen.flow_from_directory('data/train',
                                                 target_size=(120, 120),
                                                 batch_size=7,
                                                 color_mode='grayscale',
                                                 class_mode='categorical')

test_set = test_datagen.flow_from_directory('data/test',
                                            target_size=(120, 120),
                                            batch_size=7,
                                            color_mode='grayscale',
                                            class_mode='categorical')
model.fit(
        training_set,
        steps_per_epoch=125, # No of images in training set
        epochs=7,
        validation_data=test_set,
        validation_steps=50)# No of images in test set

test_loss, test_acc = model.evaluate(test_set)

print('Test accuracy: {:2.2f}%'.format(test_acc*100))

# Save entire model to a HDF5 file
model.save('handrecognition_model.hdf5')
model.summary()

# Saving the model
model_json = model.to_json()
with open("gesture-model.json", "w") as json_file:
    json_file.write(model_json)
model.save_weights('gesture-model.h5')