Moved hyper-parameter example optimization into optimization folder

examples/optimization/hyper-parameter-optimization/base_template_keras_simple.py

@@ -0,0 +1,86 @@
+# TRAINS - Keras with Tensorboard example code, automatic logging model and Tensorboard outputs
+#
+# Train a simple deep NN on the MNIST dataset.
+# Gets to 98.40% test accuracy after 20 epochs
+# (there is *a lot* of margin for parameter tuning).
+# 2 seconds per epoch on a K520 GPU.
+from __future__ import print_function
+
+import tempfile
+import os
+
+from keras.callbacks import TensorBoard, ModelCheckpoint
+from keras.datasets import mnist
+from keras.models import Sequential
+from keras.layers.core import Dense, Activation
+from keras.optimizers import RMSprop
+from keras.utils import np_utils
+import tensorflow as tf  # noqa: F401
+
+from trains import Task, Logger
+
+
+# Connecting TRAINS
+task = Task.init(project_name='examples', task_name='Keras HP optimization base')
+
+
+# the data, shuffled and split between train and test sets
+nb_classes = 10
+(X_train, y_train), (X_test, y_test) = mnist.load_data()
+
+X_train = X_train.reshape(60000, 784).astype('float32')/255.
+X_test = X_test.reshape(10000, 784).astype('float32')/255.
+print(X_train.shape[0], 'train samples')
+print(X_test.shape[0], 'test samples')
+
+# convert class vectors to binary class matrices
+Y_train = np_utils.to_categorical(y_train, nb_classes)
+Y_test = np_utils.to_categorical(y_test, nb_classes)
+
+args = {'batch_size': 128,
+        'epochs': 6,
+        'layer_1': 512,
+        'layer_2': 512,
+        'layer_3': 10,
+        'layer_4': 512,
+        }
+args = task.connect(args)
+
+model = Sequential()
+model.add(Dense(args['layer_1'], input_shape=(784,)))
+model.add(Activation('relu'))
+# model.add(Dropout(0.2))
+model.add(Dense(args['layer_2']))
+model.add(Activation('relu'))
+# model.add(Dropout(0.2))
+model.add(Dense(args['layer_3']))
+model.add(Activation('softmax'))
+
+model2 = Sequential()
+model2.add(Dense(args['layer_4'], input_shape=(784,)))
+model2.add(Activation('relu'))
+
+model.summary()
+
+model.compile(loss='categorical_crossentropy',
+              optimizer=RMSprop(),
+              metrics=['accuracy'])
+
+# Advanced: setting model class enumeration
+labels = dict(('digit_%d' % i, i) for i in range(10))
+task.set_model_label_enumeration(labels)
+
+output_folder = os.path.join(tempfile.gettempdir(), 'keras_example')
+
+board = TensorBoard(log_dir=output_folder, write_images=False)
+model_store = ModelCheckpoint(filepath=os.path.join(output_folder, 'weight.hdf5'))
+
+history = model.fit(X_train, Y_train,
+                    batch_size=args['batch_size'], epochs=args['epochs'],
+                    callbacks=[board, model_store],
+                    validation_data=(X_test, Y_test))
+score = model.evaluate(X_test, Y_test, verbose=0)
+print('Test score:', score[0])
+print('Test accuracy:', score[1])
+Logger.current_logger().report_scalar(title='evaluate', series='score', value=score[0], iteration=args['epochs'])
+Logger.current_logger().report_scalar(title='evaluate', series='accuracy', value=score[1], iteration=args['epochs'])