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Merge pull request #77 from danmalowany-allegro/master

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Update jupyter example to TensorFlow 2
This commit is contained in:
Allegro AI
2020-01-06 17:41:26 +02:00
committed by GitHub
parent be8d100e33 a3194ff17b
commit 42e3d320a6
1 changed files with 91 additions and 95 deletions
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examples/jupyter.ipynb
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@@ -3,76 +3,74 @@
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.5/dist-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
" from ._conv import register_converters as _register_converters\n",
"Using TensorFlow backend.\n"
]
}
],
"metadata": {},
"outputs": [],
"source": [
"# Trains - Example of integrating plots and training on jupyter notebook. \n",
"# In this example, simple graphs are shown, then an MNIST classifier is trained using Keras.\n",
"\n",
"from keras.callbacks import TensorBoard, ModelCheckpoint\n",
"from keras.datasets import mnist\n",
"from keras.models import Sequential\n",
"from keras.layers.core import Dense, Dropout, Activation\n",
"from keras.optimizers import SGD, Adam, RMSprop\n",
"from keras.utils import np_utils\n",
"\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n"
"# In this example, simple graphs are shown, then an MNIST classifier is trained using Keras. \n",
"!pip install -U pip\n",
"!pip install -U trains\n",
"!pip install -U numpy==1.18.0\n",
"!pip install -U tensorflow==2.0.0\n",
"!pip install -U tensorboard==2.0.0\n",
"!pip install -U matplotlib==3.1.2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.callbacks import TensorBoard, ModelCheckpoint\n",
"from tensorflow.keras.datasets import mnist\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Dense, Dropout, Activation\n",
"from tensorflow.keras.optimizers import SGD, Adam, RMSprop\n",
"from tensorflow.keras.utils import to_categorical\n",
"\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"TRAINS Task: overwriting (reusing) task id=8d23de406d0a4159a496b64c7eba0e32\n",
"======> WARNING! UNCOMMITTED CHANGES IN REPOSITORY https://github.com/allegroai/trains.git <======\n",
"TRAINS results page: https://demoapp.trainsai.io/projects/087f765c846c4c76a7e9f3d035667d82/experiments/8d23de406d0a4159a496b64c7eba0e32/output/log\n"
"TRAINS Task: created new task id=83ebee675e9f4b50af88da70be6a30d6\n",
"2020-01-05 17:51:21,909 - trains.Task - INFO - No repository found, storing script code instead\n",
"TRAINS results page: https://demoapp.trains.allegro.ai/projects/087f765c846c4c76a7e9f3d035667d82/experiments/83ebee675e9f4b50af88da70be6a30d6/output/log\n"
]
}
],
"source": [
"# Connecting TRAINS\n",
"from trains import Task\n",
"task = Task.init(project_name = 'examples', task_name = 'notebook example')\n"
"task = Task.init(project_name = 'examples', task_name = 'notebook example')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"scrolled": true
},
"outputs": [],
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"TRAINS Monitor: GPU monitoring failed getting GPU reading, switching off GPU monitoring\n"
]
}
],
"source": [
"# Set script parameters\n",
"task_params = {'num_scatter_samples': 60, 'sin_max_value': 20, 'sin_steps': 30}\n",
"task_params = task.connect(task_params)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Simple plots. You can view the plots in experiments results page "
"task_params = task.connect(task_params)"
]
},
{
@@ -140,19 +138,10 @@
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Keras training example\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"scrolled": true
},
"metadata": {},
"outputs": [],
"source": [
"# Notice, Updating task_params is traced and updated in TRAINS\n",
@@ -162,17 +151,13 @@
"task_params['hidden_dim'] = 512\n",
"batch_size = task_params['batch_size']\n",
"nb_classes = task_params['nb_classes']\n",
"nb_epoch = task_params['nb_epoch']\n"
"nb_epoch = task_params['nb_epoch']"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"metadata": {},
"outputs": [
{
"name": "stdout",
@@ -180,46 +165,59 @@
"text": [
"60000 train samples\n",
"10000 test samples\n",
"WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Colocations handled automatically by placer.\n",
"Model: \"sequential\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"dense_1 (Dense) (None, 512) 401920 \n",
"dense (Dense) (None, 512) 401920 \n",
"_________________________________________________________________\n",
"activation (Activation) (None, 512) 0 \n",
"_________________________________________________________________\n",
"dense_1 (Dense) (None, 512) 262656 \n",
"_________________________________________________________________\n",
"activation_1 (Activation) (None, 512) 0 \n",
"_________________________________________________________________\n",
"dense_2 (Dense) (None, 512) 262656 \n",
"dense_2 (Dense) (None, 10) 5130 \n",
"_________________________________________________________________\n",
"activation_2 (Activation) (None, 512) 0 \n",
"_________________________________________________________________\n",
"dense_3 (Dense) (None, 10) 5130 \n",
"_________________________________________________________________\n",
"activation_3 (Activation) (None, 10) 0 \n",
"activation_2 (Activation) (None, 10) 0 \n",
"=================================================================\n",
"Total params: 669,706\n",
"Trainable params: 669,706\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n",
"WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use tf.cast instead.\n",
"Train on 60000 samples, validate on 10000 samples\n",
"Epoch 1/6\n",
"60000/60000 [==============================] - 4s 64us/step - loss: 0.2129 - acc: 0.9350 - val_loss: 0.1012 - val_acc: 0.9682\n",
"59264/60000 [============================>.] - ETA: 0s - loss: 0.2217 - accuracy: 0.93122020-01-05 17:51:43,884 - trains - WARNING - too many indices for array\n",
"2020-01-05 17:51:43,906 - trains - WARNING - too many indices for array\n",
"2020-01-05 17:51:43,909 - trains - WARNING - too many indices for array\n",
"60000/60000 [==============================] - 6s 99us/sample - loss: 0.2206 - accuracy: 0.9316 - val_loss: 0.1300 - val_accuracy: 0.9586\n",
"Epoch 2/6\n",
"60000/60000 [==============================] - 4s 68us/step - loss: 0.0813 - acc: 0.9752 - val_loss: 0.0684 - val_acc: 0.9779\n",
"59264/60000 [============================>.] - ETA: 0s - loss: 0.0808 - accuracy: 0.97512020-01-05 17:51:49,017 - trains - WARNING - too many indices for array\n",
"2020-01-05 17:51:49,039 - trains - WARNING - too many indices for array\n",
"2020-01-05 17:51:49,043 - trains - WARNING - too many indices for array\n",
"60000/60000 [==============================] - 5s 84us/sample - loss: 0.0804 - accuracy: 0.9752 - val_loss: 0.0794 - val_accuracy: 0.9765\n",
"Epoch 3/6\n",
"60000/60000 [==============================] - 4s 62us/step - loss: 0.0540 - acc: 0.9830 - val_loss: 0.0736 - val_acc: 0.9793\n",
"59648/60000 [============================>.] - ETA: 0s - loss: 0.0542 - accuracy: 0.98342020-01-05 17:51:54,222 - trains - WARNING - too many indices for array\n",
"2020-01-05 17:51:54,245 - trains - WARNING - too many indices for array\n",
"2020-01-05 17:51:54,248 - trains - WARNING - too many indices for array\n",
"60000/60000 [==============================] - 5s 87us/sample - loss: 0.0540 - accuracy: 0.9834 - val_loss: 0.0758 - val_accuracy: 0.9782\n",
"Epoch 4/6\n",
"60000/60000 [==============================] - 4s 64us/step - loss: 0.0387 - acc: 0.9880 - val_loss: 0.0859 - val_acc: 0.9761\n",
"59392/60000 [============================>.] - ETA: 0s - loss: 0.0388 - accuracy: 0.98762020-01-05 17:51:59,298 - trains - WARNING - too many indices for array\n",
"2020-01-05 17:51:59,320 - trains - WARNING - too many indices for array\n",
"2020-01-05 17:51:59,324 - trains - WARNING - too many indices for array\n",
"60000/60000 [==============================] - 5s 84us/sample - loss: 0.0387 - accuracy: 0.9876 - val_loss: 0.0836 - val_accuracy: 0.9777\n",
"Epoch 5/6\n",
"60000/60000 [==============================] - 4s 63us/step - loss: 0.0304 - acc: 0.9904 - val_loss: 0.0875 - val_acc: 0.9766\n",
"59520/60000 [============================>.] - ETA: 0s - loss: 0.0282 - accuracy: 0.99142020-01-05 17:52:04,410 - trains - WARNING - too many indices for array\n",
"2020-01-05 17:52:04,433 - trains - WARNING - too many indices for array\n",
"2020-01-05 17:52:04,436 - trains - WARNING - too many indices for array\n",
"60000/60000 [==============================] - 5s 85us/sample - loss: 0.0280 - accuracy: 0.9915 - val_loss: 0.0754 - val_accuracy: 0.9811\n",
"Epoch 6/6\n",
"60000/60000 [==============================] - 4s 64us/step - loss: 0.0220 - acc: 0.9933 - val_loss: 0.0847 - val_acc: 0.9793\n",
"Test score: 0.08471047468512916\n",
"Test accuracy: 0.9793\n"
"59520/60000 [============================>.] - ETA: 0s - loss: 0.0242 - accuracy: 0.99252020-01-05 17:52:09,482 - trains - WARNING - too many indices for array\n",
"2020-01-05 17:52:09,504 - trains - WARNING - too many indices for array\n",
"2020-01-05 17:52:09,507 - trains - WARNING - too many indices for array\n",
"60000/60000 [==============================] - 5s 85us/sample - loss: 0.0243 - accuracy: 0.9924 - val_loss: 0.0769 - val_accuracy: 0.9824\n",
"Test score: 0.07691321085649504\n",
"Test accuracy: 0.9824\n"
]
}
],
@@ -237,8 +235,8 @@
"print(X_test.shape[0], 'test samples')\n",
"\n",
"# 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",
"Y_train = to_categorical(y_train, nb_classes)\n",
"Y_test = to_categorical(y_test, nb_classes)\n",
"\n",
"hidden_dim = task_params['hidden_dim']\n",
"model = Sequential()\n",
@@ -266,15 +264,22 @@
" verbose=1, 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])\n"
"print('Test accuracy:', score[1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "conda_python3",
"language": "python",
"name": "python3"
"name": "conda_python3"
},
"language_info": {
"codemirror_mode": {
@@ -286,16 +291,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
},
"pycharm": {
"stem_cell": {
"cell_type": "raw",
"metadata": {
"collapsed": false
},
"source": []
}
"version": "3.6.9"
}
},
"nbformat": 4,