alpha-model_train.py

import tensorflow as tf
from keras.models import Sequential
from keras.layers import Convolution2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense, Dropout
import os

os.environ['CUDA_VISIBLE_DEVICES'] = "0"
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
    try:
        tf.config.experimental.set_virtual_device_configuration(gpus[0], [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=2048)])
    except RuntimeError as e:
        print(e)

sz = 128
# Step 1 - Building the CNN

# Initializing the CNN
classifier = Sequential()

classifier.add(Convolution2D(32, (3, 3), input_shape=(sz, sz, 1), activation='relu'))
classifier.add(MaxPooling2D(pool_size=(2, 2)))
classifier.add(Convolution2D(64, (3, 3), activation='relu'))
classifier.add(MaxPooling2D(pool_size=(2, 2)))
classifier.add(Flatten())

# Adding a fully connected layer
classifier.add(Dense(units=128, activation='relu'))
classifier.add(Dropout(0.20))
classifier.add(Dense(units=112, activation='relu'))
classifier.add(Dropout(0.10))
classifier.add(Dense(units=96, activation='relu'))
classifier.add(Dropout(0.10))
classifier.add(Dense(units=80, activation='relu'))
classifier.add(Dropout(0.10))
classifier.add(Dense(units=64, activation='relu'))
classifier.add(Dense(units=26, activation='softmax')) # softmax for more than 2

# Compiling the CNN
classifier.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

classifier.summary()
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('dataset-alpha/train',
                                                 target_size=(sz, sz),
                                                 batch_size=10,
                                                 color_mode='grayscale',
                                                 class_mode='categorical')

test_set = test_datagen.flow_from_directory('dataset-alpha/test',
                                            target_size=(sz , sz),
                                            batch_size=10,
                                            color_mode='grayscale',
                                            class_mode='categorical')
classifier.fit_generator(
        training_set,
        steps_per_epoch=training_set.n//training_set.batch_size, # No of images in training set/batch size
        epochs=10,
        validation_data=test_set,
        validation_steps=test_set.n//test_set.batch_size)# No of images in test set/batch size

classifier.save("model-all1-alpha.h5")
print('Model Saved')
print(training_set.n//training_set.batch_size)
print(test_set.n//test_set.batch_size)