Slack integeration

slack_interaction/utils.py

@@ -0,0 +1,80 @@
+import keras
+from keras.datasets import mnist
+from keras.models import Sequential
+from keras.layers import Dense, Dropout, Flatten
+from keras.layers import Conv2D, MaxPooling2D
+from keras import backend as K
+
+
+def get_options(slack):
+    command_dict = {'functions': {},
+                    'attributes': {}}
+
+    # Modules
+    for d in dir(slack):
+        if not d.startswith('_'):
+            command_dict['functions'][d] = []
+            command_dict['attributes'][d] = []
+            # Iterate through methods and attributes
+            for dd in dir(getattr(slack, d)):
+                if not dd.startswith('_'):
+                    # List of methods and attributes
+                    l = dir(getattr(getattr(slack, d), dd))
+                    # Method (function)
+                    if '__call__' in l:
+                        command_dict['functions'][d].append(dd)
+                    # Attributes
+                    else:
+                        command_dict['attributes'][d].append(dd)
+                        
+    return command_dict
+
+def get_data_and_model():
+    batch_size = 128
+    num_classes = 10
+    epochs = 12
+
+    # input image dimensions
+    img_rows, img_cols = 28, 28
+
+    # the data, split between train and test sets
+    (x_train, y_train), (x_test, y_test) = mnist.load_data()
+
+    if K.image_data_format() == 'channels_first':
+        x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
+        x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
+        input_shape = (1, img_rows, img_cols)
+    else:
+        x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
+        x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
+        input_shape = (img_rows, img_cols, 1)
+
+    x_train = x_train.astype('float32')
+    x_test = x_test.astype('float32')
+    x_train /= 255
+    x_test /= 255
+    print('x_train shape:', x_train.shape)
+    print(x_train.shape[0], 'train samples')
+    print(x_test.shape[0], 'test samples')
+
+    # convert class vectors to binary class matrices
+    y_train = keras.utils.to_categorical(y_train, num_classes)
+    y_test = keras.utils.to_categorical(y_test, num_classes)
+
+    model = Sequential()
+    model.add(Conv2D(32, kernel_size=(3, 3),
+                     activation='relu',
+                     input_shape=input_shape))
+    model.add(Conv2D(64, (3, 3), activation='relu'))
+    model.add(MaxPooling2D(pool_size=(2, 2)))
+    model.add(Dropout(0.25))
+    model.add(Flatten())
+    model.add(Dense(128, activation='relu'))
+    model.add(Dropout(0.5))
+    model.add(Dense(num_classes, activation='softmax'))
+
+    model.compile(loss=keras.losses.categorical_crossentropy,
+                  optimizer=keras.optimizers.Adadelta(),
+                  metrics=['accuracy'])
+    
+    return x_train, x_test, y_train, y_test, model