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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')
+