Add example notebooks, update example requirements

examples/frameworks/keras/Allegro_Trains_keras_TB_example.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "wFJPLbY7w7Vj"
+   },
+   "source": [
+    "# Allegro Trains Keras with Tensorboard example\n",
+    "\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/allegroai/trains/blob/master/examples/frameworks/keras/Allegro_Trains_keras_TB_example.ipynb)\n",
+    "\n",
+    "This tutorial introduce Trains with Keras and Tensorboard functionality. automatic logging model and Tensorboard outputs. You can find more frameworks examples [here](https://github.com/allegroai/trains/tree/master/examples/frameworks).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "K7HA0KcX3XBf"
+   },
+   "outputs": [],
+   "source": [
+    "!pip install trains\n",
+    "!pip install tensorflow>=2.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "ZM0EIh-GqZuu"
+   },
+   "source": [
+    "### Create a new task.\n",
+    "Task object should be provided `project_name` (the project name for the experiment) and `task_name` (the name of the experiment). A link to the newly generated task will be printed and the task will be updated real time in the Trains demo server.\n",
+    "\n",
+    "You can read about task in the docs [here](https://allegro.ai/docs/task.html)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "RYXhcm58uVGL"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import tempfile\n",
+    "\n",
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "from tensorflow import keras\n",
+    "from tensorflow.keras import utils as np_utils\n",
+    "from trains import Task\n",
+    "\n",
+    "# Start a new task\n",
+    "task = Task.init(project_name=\"Colab notebooks\", task_name=\"Keras with TensorBoard example\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "GPLPiHQ1ygTg"
+   },
+   "source": [
+    "*Based on https://github.com/keras-team/keras/blob/master/examples/mnist_mlp.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "6A36rDJ7s5Pb"
+   },
+   "outputs": [],
+   "source": [
+    "# Train a simple deep NN on the MNIST dataset.\n",
+    "# Gets to 98.40% test accuracy after 20 epochs\n",
+    "# (there is *a lot* of margin for parameter tuning).\n",
+    "# 2 seconds per epoch on a K520 GPU.\n",
+    "\n",
+    "\n",
+    "class TensorBoardImage(keras.callbacks.TensorBoard):\n",
+    "    @staticmethod\n",
+    "    def make_image(tensor):\n",
+    "        from PIL import Image\n",
+    "        import io\n",
+    "        tensor = np.stack((tensor, tensor, tensor), axis=2)\n",
+    "        height, width, channels = tensor.shape\n",
+    "        image = Image.fromarray(tensor)\n",
+    "        output = io.BytesIO()\n",
+    "        image.save(output, format='PNG')\n",
+    "        image_string = output.getvalue()\n",
+    "        output.close()\n",
+    "        return tf.Summary.Image(height=height,\n",
+    "                                width=width,\n",
+    "                                colorspace=channels,\n",
+    "                                encoded_image_string=image_string)\n",
+    "\n",
+    "    def on_epoch_end(self, epoch, logs=None):\n",
+    "        if logs is None:\n",
+    "            logs = {}\n",
+    "        super(TensorBoardImage, self).on_epoch_end(epoch, logs)\n",
+    "        images = self.validation_data[0]  # 0 - data; 1 - labels\n",
+    "        img = (255 * images[0].reshape(28, 28)).astype('uint8')\n",
+    "\n",
+    "        image = self.make_image(img)\n",
+    "        summary = tf.Summary(value=[tf.Summary.Value(tag='image', image=image)])\n",
+    "        self.writer.add_summary(summary, epoch)\n",
+    "\n",
+    "\n",
+    "# the data, shuffled and split between train and test sets\n",
+    "nb_classes = 10\n",
+    "(X_train, y_train), (X_test, y_test) = keras.datasets.mnist.load_data()\n",
+    "\n",
+    "X_train = X_train.reshape(60000, 784).astype('float32') / 255.\n",
+    "X_test = X_test.reshape(10000, 784).astype('float32') / 255.\n",
+    "print(X_train.shape[0], 'train samples')\n",
+    "print(X_test.shape[0], 'test samples')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "gFMDBxwN4nR2"
+   },
+   "outputs": [],
+   "source": [
+    "# convert class vectors to binary class matrices\n",
+    "Y_train = np_utils.to_categorical(y_train, nb_classes)\n",
+    "Y_test = np_utils.to_categorical(y_test, nb_classes)\n",
+    "\n",
+    "model = keras.models.Sequential()\n",
+    "model.add(keras.layers.Dense(512, input_shape=(784,)))\n",
+    "model.add(keras.layers.Activation('relu'))\n",
+    "\n",
+    "model.add(keras.layers.Dense(512))\n",
+    "model.add(keras.layers.Activation('relu'))\n",
+    "\n",
+    "model.add(keras.layers.Dense(10))\n",
+    "model.add(keras.layers.Activation('softmax'))\n",
+    "\n",
+    "model2 = keras.models.Sequential()\n",
+    "model2.add(keras.layers.Dense(512, input_shape=(784,)))\n",
+    "model2.add(keras.layers.Activation('relu'))\n",
+    "\n",
+    "model.summary()\n",
+    "\n",
+    "model.compile(loss='categorical_crossentropy',\n",
+    "              optimizer=keras.optimizers.RMSprop(),\n",
+    "              metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "40iQp_Wq4K28"
+   },
+   "outputs": [],
+   "source": [
+    "# Advanced: setting model class enumeration and set it for the task\n",
+    "labels = dict(('digit_%d' % i, i) for i in range(10))\n",
+    "task.set_model_label_enumeration(labels)\n",
+    "\n",
+    "output_folder = os.path.join(tempfile.gettempdir(), 'keras_example')\n",
+    "\n",
+    "board = keras.callbacks.TensorBoard(histogram_freq=1, log_dir=output_folder, write_images=False)\n",
+    "model_store = keras.callbacks.ModelCheckpoint(filepath=os.path.join(output_folder, 'weight.{epoch}.hdf5'))\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "5FIKDIzy4YF6"
+   },
+   "outputs": [],
+   "source": [
+    "# Fit and evaluate the model\n",
+    "\n",
+    "history = model.fit(X_train,\n",
+    "                    Y_train,\n",
+    "                    batch_size=128,\n",
+    "                    epochs=6,\n",
+    "                    callbacks=[board, model_store],\n",
+    "                    verbose=1,\n",
+    "                    validation_data=(X_test, Y_test))\n",
+    "score = model.evaluate(X_test, Y_test, verbose=0)\n",
+    "print('Test score:', score[0])\n",
+    "print('Test accuracy:', score[1])"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "collapsed_sections": [],
+   "name": "Allegro Trains keras TB example.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}