mirror of
https://github.com/clearml/clearml
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240 lines
7.6 KiB
Plaintext
240 lines
7.6 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "wFJPLbY7w7Vj"
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},
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"source": [
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"# Allegro ClearML Keras with TensorBoard example\n",
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"\n",
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"[](https://colab.research.google.com/github/allegroai/clearml/blob/master/examples/frameworks/keras/jupyter_keras_TB_example.ipynb)\n",
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"\n",
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"This example introduces ClearML with Keras and TensorBoard functionality, including automatic logging, models, and TensorBoard outputs. You can find more frameworks examples [here](https://github.com/allegroai/clearml/tree/master/examples/frameworks).\n",
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"\n",
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"Note: This example is based on the Keras `mnist_mlp.py` example.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "K7HA0KcX3XBf"
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},
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"outputs": [],
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"source": [
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"# If you don't have ClearML installed then uncomment this line\n",
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"# !pip install clearml"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install tensorflow>=2.0"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "ZM0EIh-GqZuu"
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},
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"source": [
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"### Create a new task.\n",
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"To create a new Task object, call the `Task.init` method providing it with `project_name` (the project name for the experiment) and `task_name` (the name of the experiment). When `Task.init` executes, a link to the Web UI Results page for the newly generated Task will be printed, and the Task will be updated in real time in the ClearML demo server.\n",
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"\n",
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"You can read about the `Task` class in the docs [here](https://clear.ml/docs/latest/docs/fundamentals/task)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "RYXhcm58uVGL"
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},
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"outputs": [],
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"source": [
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"import os\n",
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"import tempfile\n",
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"\n",
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"import numpy as np\n",
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"import tensorflow as tf\n",
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"from tensorflow import keras\n",
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"from tensorflow.keras import utils as np_utils\n",
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"from clearml import Task\n",
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"\n",
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"# Start a new task\n",
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"task = Task.init(project_name=\"Colab notebooks\", task_name=\"Keras with TensorBoard example\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "6A36rDJ7s5Pb"
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},
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"outputs": [],
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"source": [
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"# Train a simple deep NN on the MNIST dataset.\n",
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"# Gets to 98.40% test accuracy after 20 epochs\n",
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"# (there is *a lot* of margin for parameter tuning).\n",
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"# 2 seconds per epoch on a K520 GPU.\n",
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"\n",
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"\n",
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"class TensorBoardImage(keras.callbacks.TensorBoard):\n",
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" @staticmethod\n",
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" def make_image(tensor):\n",
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" from PIL import Image\n",
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" import io\n",
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" tensor = np.stack((tensor, tensor, tensor), axis=2)\n",
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" height, width, channels = tensor.shape\n",
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" image = Image.fromarray(tensor)\n",
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" output = io.BytesIO()\n",
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" image.save(output, format='PNG')\n",
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" image_string = output.getvalue()\n",
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" output.close()\n",
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" return tf.Summary.Image(height=height,\n",
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" width=width,\n",
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" colorspace=channels,\n",
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" encoded_image_string=image_string)\n",
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"\n",
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" def on_epoch_end(self, epoch, logs=None):\n",
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" if logs is None:\n",
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" logs = {}\n",
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" super(TensorBoardImage, self).on_epoch_end(epoch, logs)\n",
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" images = self.validation_data[0] # 0 - data; 1 - labels\n",
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" img = (255 * images[0].reshape(28, 28)).astype('uint8')\n",
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"\n",
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" image = self.make_image(img)\n",
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" summary = tf.Summary(value=[tf.Summary.Value(tag='image', image=image)])\n",
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" self.writer.add_summary(summary, epoch)\n",
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"\n",
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"\n",
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"# the data, shuffled and split between train and test sets\n",
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"nb_classes = 10\n",
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"(X_train, y_train), (X_test, y_test) = keras.datasets.mnist.load_data()\n",
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"\n",
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"X_train = X_train.reshape(60000, 784).astype('float32') / 255.\n",
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"X_test = X_test.reshape(10000, 784).astype('float32') / 255.\n",
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"print(X_train.shape[0], 'train samples')\n",
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"print(X_test.shape[0], 'test samples')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "gFMDBxwN4nR2"
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},
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"outputs": [],
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"source": [
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"# convert class vectors to binary class matrices\n",
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"Y_train = np_utils.to_categorical(y_train, nb_classes)\n",
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"Y_test = np_utils.to_categorical(y_test, nb_classes)\n",
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"\n",
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"model = keras.models.Sequential()\n",
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"model.add(keras.layers.Dense(512, input_shape=(784,)))\n",
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"model.add(keras.layers.Activation('relu'))\n",
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"\n",
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"model.add(keras.layers.Dense(512))\n",
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"model.add(keras.layers.Activation('relu'))\n",
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"\n",
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"model.add(keras.layers.Dense(10))\n",
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"model.add(keras.layers.Activation('softmax'))\n",
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"\n",
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"model2 = keras.models.Sequential()\n",
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"model2.add(keras.layers.Dense(512, input_shape=(784,)))\n",
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"model2.add(keras.layers.Activation('relu'))\n",
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"\n",
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"model.summary()\n",
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"\n",
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"model.compile(loss='categorical_crossentropy',\n",
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" optimizer=keras.optimizers.RMSprop(),\n",
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" metrics=['accuracy'])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "40iQp_Wq4K28"
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},
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"outputs": [],
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"source": [
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"# Advanced: setting model class enumeration and set it for the task\n",
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"labels = dict(('digit_%d' % i, i) for i in range(10))\n",
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"task.set_model_label_enumeration(labels)\n",
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"\n",
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"output_folder = os.path.join(tempfile.gettempdir(), 'keras_example')\n",
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"\n",
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"board = keras.callbacks.TensorBoard(histogram_freq=1, log_dir=output_folder, write_images=False)\n",
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"model_store = keras.callbacks.ModelCheckpoint(filepath=os.path.join(output_folder, 'weight.{epoch}.hdf5'))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "5FIKDIzy4YF6"
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},
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"outputs": [],
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"source": [
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"# Fit and evaluate the model\n",
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"\n",
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"history = model.fit(X_train,\n",
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" Y_train,\n",
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" batch_size=128,\n",
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" epochs=6,\n",
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" callbacks=[board, model_store],\n",
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" verbose=1,\n",
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" validation_data=(X_test, Y_test))\n",
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"score = model.evaluate(X_test, Y_test, verbose=0)\n",
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"print('Test score:', score[0])\n",
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"print('Test accuracy:', score[1])"
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]
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}
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],
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"metadata": {
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"colab": {
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"collapsed_sections": [],
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"name": "Allegro ClearML keras TB example.ipynb",
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}
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