Add template scripts

Author: afshinea

keras_script.py

@@ -0,0 +1,30 @@
+import numpy as np
+
+from keras.models import Sequential
+from my_classes import DataGenerator
+
+# Parameters
+params = {'dim': (32,32,32),
+          'batch_size': 64,
+          'n_classes': 6,
+          'n_channels': 1,
+          'shuffle': True}
+
+# Datasets
+partition = {'train': ['id-1', 'id-2', 'id-3'], 'validation': ['id-4']} # IDs
+labels = {'id-1': 0, 'id-2': 1, 'id-3': 2, 'id-4': 1} # Labels
+
+# Generators
+training_generator = DataGenerator(partition['train'], labels, **params)
+validation_generator = DataGenerator(partition['validation'], labels, **params)
+
+# Design model
+model = Sequential()
+[...] # Architecture
+model.compile()
+
+# Train model on dataset
+model.fit_generator(generator=training_generator,
+                    validation_data=validation_generator,
+                    use_multiprocessing=True,
+                    workers=6)

my_classes.py

@@ -0,0 +1,56 @@
+import numpy as np
+import keras
+
+
+class DataGenerator(keras.utils.Sequence):
+    'Generates data for Keras'
+    def __init__(self, list_IDs, labels, batch_size=32, dim=(32,32,32), n_channels=1,
+                 n_classes=10, shuffle=True):
+        'Initialization'
+        self.dim = dim
+        self.batch_size = batch_size
+        self.labels = labels
+        self.list_IDs = list_IDs
+        self.n_channels = n_channels
+        self.n_classes = n_classes
+        self.shuffle = shuffle
+        self.on_epoch_end()
+
+    def __len__(self):
+        'Denotes the number of batches per epoch'
+        return int(np.floor(len(self.list_IDs) / self.batch_size))
+
+    def __getitem__(self, index):
+        'Generate one batch of data'
+        # Generate indexes of the batch
+        indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size]
+
+        # Find list of IDs
+        list_IDs_temp = [self.list_IDs[k] for k in indexes]
+
+        # Generate data
+        X, y = self.__data_generation(list_IDs_temp)
+
+        return X, y
+
+    def on_epoch_end(self):
+        'Updates indexes after each epoch'
+        self.indexes = np.arange(len(self.list_IDs))
+        if self.shuffle == True:
+            np.random.shuffle(self.indexes)
+
+    def __data_generation(self, list_IDs_temp):
+        'Generates data containing batch_size samples' # X : (n_samples, *dim, n_channels)
+        # Initialization
+        X = np.empty((self.batch_size, *self.dim, self.n_channels))
+        y = np.empty((self.batch_size), dtype=int)
+
+        # Generate data
+        for i, ID in enumerate(list_IDs_temp):
+            # Store sample
+            X[i,] = np.load('data/' + ID + '.npy')
+
+            # Store class
+            y[i] = self.labels[ID]
+
+        return X, keras.utils.to_categorical(y, num_classes=self.n_classes)