Refactor examples

examples/frameworks/keras/keras_tensorboard.py

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+# 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 argparse
+import os
+import tempfile
+
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras import utils as np_utils
+from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard
+from tensorflow.keras.datasets import mnist
+from tensorflow.keras.layers import Activation, Dense
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.optimizers import RMSprop
+
+from trains import Task
+
+
+class TensorBoardImage(TensorBoard):
+    @staticmethod
+    def make_image(tensor):
+        from PIL import Image
+        import io
+        tensor = np.stack((tensor, tensor, tensor), axis=2)
+        height, width, channels = tensor.shape
+        image = Image.fromarray(tensor)
+        output = io.BytesIO()
+        image.save(output, format='PNG')
+        image_string = output.getvalue()
+        output.close()
+        return tf.Summary.Image(height=height,
+                                width=width,
+                                colorspace=channels,
+                                encoded_image_string=image_string)
+
+    def on_epoch_end(self, epoch, logs=None):
+        if logs is None:
+            logs = {}
+        super(TensorBoardImage, self).on_epoch_end(epoch, logs)
+        images = self.validation_data[0]  # 0 - data; 1 - labels
+        img = (255 * images[0].reshape(28, 28)).astype('uint8')
+
+        image = self.make_image(img)
+        summary = tf.Summary(value=[tf.Summary.Value(tag='image', image=image)])
+        self.writer.add_summary(summary, epoch)
+
+
+parser = argparse.ArgumentParser(description='Keras MNIST Example')
+parser.add_argument('--batch-size', type=int, default=128, help='input batch size for training (default: 128)')
+parser.add_argument('--epochs', type=int, default=6, help='number of epochs to train (default: 6)')
+args = parser.parse_args()
+
+# 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)
+
+model = Sequential()
+model.add(Dense(512, input_shape=(784,)))
+model.add(Activation('relu'))
+# model.add(Dropout(0.2))
+model.add(Dense(512))
+model.add(Activation('relu'))
+# model.add(Dropout(0.2))
+model.add(Dense(10))
+model.add(Activation('softmax'))
+
+model2 = Sequential()
+model2.add(Dense(512, input_shape=(784,)))
+model2.add(Activation('relu'))
+
+model.summary()
+
+model.compile(loss='categorical_crossentropy',
+              optimizer=RMSprop(),
+              metrics=['accuracy'])
+
+# Connecting TRAINS
+task = Task.init(project_name='examples', task_name='Keras with TensorBoard example')
+task.connect_configuration({'test': 1337, 'nested': {'key': 'value', 'number': 1}})
+
+# 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(histogram_freq=1, log_dir=output_folder, write_images=False)
+model_store = ModelCheckpoint(filepath=os.path.join(output_folder, 'weight.{epoch}.hdf5'))
+
+# load previous model, if it is there
+# noinspection PyBroadException
+try:
+    model.load_weights(os.path.join(output_folder, 'weight.1.hdf5'))
+except Exception:
+    pass
+
+history = model.fit(X_train, Y_train,
+                    batch_size=args.batch_size, epochs=args.epochs,
+                    callbacks=[board, model_store],
+                    verbose=1, 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])

examples/frameworks/keras/legacy/keras_tensorboard.py

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+# 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 argparse
+import tempfile
+import os
+import numpy as np
+
+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
+from trains import Task
+
+
+class TensorBoardImage(TensorBoard):
+    @staticmethod
+    def make_image(tensor):
+        from PIL import Image
+        import io
+        tensor = np.stack((tensor, tensor, tensor), axis=2)
+        height, width, channels = tensor.shape
+        image = Image.fromarray(tensor)
+        output = io.BytesIO()
+        image.save(output, format='PNG')
+        image_string = output.getvalue()
+        output.close()
+        return tf.Summary.Image(height=height,
+                                width=width,
+                                colorspace=channels,
+                                encoded_image_string=image_string)
+
+    def on_epoch_end(self, epoch, logs=None):
+        if logs is None:
+            logs = {}
+        super(TensorBoardImage, self).on_epoch_end(epoch, logs)
+        images = self.validation_data[0]  # 0 - data; 1 - labels
+        img = (255 * images[0].reshape(28, 28)).astype('uint8')
+
+        image = self.make_image(img)
+        summary = tf.Summary(value=[tf.Summary.Value(tag='image', image=image)])
+        self.writer.add_summary(summary, epoch)
+
+
+parser = argparse.ArgumentParser(description='Keras MNIST Example')
+parser.add_argument('--batch-size', type=int, default=128, help='input batch size for training (default: 128)')
+parser.add_argument('--epochs', type=int, default=6, help='number of epochs to train (default: 6)')
+args = parser.parse_args()
+
+# 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)
+
+model = Sequential()
+model.add(Dense(512, input_shape=(784,)))
+model.add(Activation('relu'))
+# model.add(Dropout(0.2))
+model.add(Dense(512))
+model.add(Activation('relu'))
+# model.add(Dropout(0.2))
+model.add(Dense(10))
+model.add(Activation('softmax'))
+
+model2 = Sequential()
+model2.add(Dense(512, input_shape=(784,)))
+model2.add(Activation('relu'))
+
+model.summary()
+
+model.compile(loss='categorical_crossentropy',
+              optimizer=RMSprop(),
+              metrics=['accuracy'])
+
+# Connecting TRAINS
+task = Task.init(project_name='examples', task_name='Keras with TensorBoard example')
+task.connect_configuration({'test': 1337, 'nested': {'key': 'value', 'number': 1}})
+
+# 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(histogram_freq=1, log_dir=output_folder, write_images=False)
+model_store = ModelCheckpoint(filepath=os.path.join(output_folder, 'weight.{epoch}.hdf5'))
+
+# load previous model, if it is there
+# noinspection PyBroadException
+try:
+    model.load_weights(os.path.join(output_folder, 'weight.1.hdf5'))
+except Exception:
+    pass
+
+history = model.fit(X_train, Y_train,
+                    batch_size=args.batch_size, epochs=args.epochs,
+                    callbacks=[board, model_store],
+                    verbose=1, 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])

examples/frameworks/keras/legacy/requirements.txt

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+trains
+Keras>=2.2.4

examples/frameworks/keras/manual_model_upload.py

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+# TRAINS - Example of manual model configuration and uploading
+#
+import os
+from tempfile import gettempdir
+
+from keras import Input, layers, Model
+
+from trains import Task
+
+
+task = Task.init(project_name='examples', task_name='Model configuration and upload')
+
+
+def get_model():
+    # Create a simple model.
+    inputs = Input(shape=(32,))
+    outputs = layers.Dense(1)(inputs)
+    keras_model = Model(inputs, outputs)
+    keras_model.compile(optimizer='adam', loss='mean_squared_error')
+    return keras_model
+
+
+# create a model
+model = get_model()
+
+# Connect a local configuration file
+config_file = os.path.join('..', '..', 'reporting', 'data_samples', 'sample.json')
+config_file = task.connect_configuration(config_file)
+# then read configuration as usual, the backend will contain a copy of it.
+# later when executing remotely, the returned `config_file` will be a temporary file
+# containing a new copy of the configuration retrieved form the backend
+# # model_config_dict = json.load(open(config_file, 'rt'))
+
+# Or Store dictionary of definition for a specific network design
+model_config_dict = {
+    'value': 13.37,
+    'dict': {'sub_value': 'string', 'sub_integer': 11},
+    'list_of_ints': [1, 2, 3, 4],
+}
+model_config_dict = task.connect_configuration(model_config_dict)
+
+# We now update the dictionary after connecting it, and the changes will be tracked as well.
+model_config_dict['new value'] = 10
+model_config_dict['value'] *= model_config_dict['new value']
+
+# store the label enumeration of the training model
+labels = {'background': 0, 'cat': 1, 'dog': 2}
+task.connect_label_enumeration(labels)
+
+# storing the model, it will have the task network configuration and label enumeration
+print('Any model stored from this point onwards, will contain both model_config and label_enumeration')
+
+model.save(os.path.join(gettempdir(), "model"))
+print('Model saved')

examples/frameworks/keras/requirements.txt

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+Keras
+tensorflow>=2.0
+trains