Delete train.py

train.py

-# Importing the Keras libraries and packages
-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"] = "1"
-sz = 128
-# Step 1 - Building the CNN
-
-# Initializing the CNN
-classifier = Sequential()
-
-# First convolution layer and pooling
-classifier.add(Convolution2D(32, (3, 3), input_shape=(sz, sz, 1), activation='relu'))
-classifier.add(MaxPooling2D(pool_size=(2, 2)))
-# Second convolution layer and pooling
-classifier.add(Convolution2D(32, (3, 3), activation='relu'))
-# input_shape is going to be the pooled feature maps from the previous convolution layer
-classifier.add(MaxPooling2D(pool_size=(2, 2)))
-#classifier.add(Convolution2D(32, (3, 3), activation='relu'))
-# input_shape is going to be the pooled feature maps from the previous convolution layer
-#classifier.add(MaxPooling2D(pool_size=(2, 2)))
-
-# Flattening the layers
-classifier.add(Flatten())
-
-# Adding a fully connected layer
-classifier.add(Dense(units=128, activation='relu'))
-classifier.add(Dropout(0.40))
-classifier.add(Dense(units=96, activation='relu'))
-classifier.add(Dropout(0.40))
-classifier.add(Dense(units=64, activation='relu'))
-classifier.add(Dense(units=27, activation='softmax')) # softmax for more than 2
-
-# Compiling the CNN
-classifier.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) # categorical_crossentropy for more than 2
-
-
-# Step 2 - Preparing the train/test data and training the model
-classifier.summary()
-# 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=True)
-
-test_datagen = ImageDataGenerator(rescale=1./255)
-
-training_set = train_datagen.flow_from_directory('data2/train',
-                                                 target_size=(sz, sz),
-                                                 batch_size=10,
-                                                 color_mode='grayscale',
-                                                 class_mode='categorical')
-
-test_set = test_datagen.flow_from_directory('data2/test',
-                                            target_size=(sz , sz),
-                                            batch_size=10,
-                                            color_mode='grayscale',
-                                            class_mode='categorical') 
-classifier.fit_generator(
-        training_set,
-        steps_per_epoch=12841, # No of images in training set
-        epochs=5,
-        validation_data=test_set,
-        validation_steps=4268)# No of images in test set
-
-
-# Saving the model
-model_json = classifier.to_json()
-with open("model-bw.json", "w") as json_file:
-    json_file.write(model_json)
-print('Model Saved')
-classifier.save_weights('model-bw.h5')
-print('Weights saved')
-