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finished blog post 1
This commit is contained in:
Abubakar Abid
parent
c70235bca8
commit
a6ece554f9
152
Test Keras MNIST.ipynb
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152
Test Keras MNIST.ipynb
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@ -0,0 +1,152 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import tensorflow as tf\n",
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"import gradio"
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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": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"(x_train, y_train),(x_test, y_test) = tf.keras.datasets.mnist.load_data()\n",
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"x_train, x_test = x_train / 255.0, x_test / 255.0"
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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": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"model = tf.keras.models.Sequential([\n",
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" tf.keras.layers.Flatten(),\n",
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" tf.keras.layers.Dense(512, activation=tf.nn.relu),\n",
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" tf.keras.layers.Dropout(0.2),\n",
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" tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n",
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"])\n",
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"\n",
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"model.compile(optimizer='adam',\n",
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" loss='sparse_categorical_crossentropy',\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": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 1/1\n",
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"60000/60000 [==============================] - 25s 417us/step - loss: 0.2210 - acc: 0.9351\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"<tensorflow.python.keras.callbacks.History at 0x22d334d8b00>"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"model.fit(x_train, y_train, epochs=1)"
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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": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"iface = gradio.Interface(inputs=\"sketchpad\", outputs=\"label\", model=model, model_type='keras')"
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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": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"No validation samples for this interface... skipping validation.\n",
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"NOTE: Gradio is in beta stage, please report all bugs to: contact.gradio@gmail.com\n",
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"Model is running locally at: http://localhost:7861/\n",
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"To create a public link, set `share=True` in the argument to `launch()`\n"
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]
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},
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{
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"data": {
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"text/html": [
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"\n",
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" <iframe\n",
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" width=\"1000\"\n",
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" height=\"500\"\n",
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" src=\"http://localhost:7861/\"\n",
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" frameborder=\"0\"\n",
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" allowfullscreen\n",
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" ></iframe>\n",
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" "
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],
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"text/plain": [
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"<IPython.lib.display.IFrame at 0x22d2cf1e710>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"(<gradio.networking.serve_files_in_background.<locals>.HTTPServer at 0x22d348a7240>,\n",
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" 'http://localhost:7861/',\n",
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" None)"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"iface.launch(inline=True, share=False)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3.6 (tensorflow)",
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"language": "python",
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"name": "tensorflow"
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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": "3.6.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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295
Test Keras.ipynb
295
Test Keras.ipynb
@ -43,290 +43,29 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"from PIL import Image\n",
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"import requests\n",
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"from io import BytesIO\n",
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"# from PIL import Image\n",
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"# import requests\n",
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"# from io import BytesIO\n",
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"\n",
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"url = 'https://nationalzoo.si.edu/sites/default/files/animals/cheetah-004.jpg'\n",
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"# url = 'https://nationalzoo.si.edu/sites/default/files/animals/cheetah-004.jpg'\n",
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"\n",
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"response = requests.get(url)\n",
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"img = Image.open(BytesIO(response.content))\n",
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"# response = requests.get(url)\n",
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"# img = Image.open(BytesIO(response.content))\n",
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"\n",
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"# resize the image into an array that the model can accept\n",
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"img = np.array(img.resize((299, 299))).reshape((1, 299, 299, 3))\n",
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"# # resize the image into an array that the model can accept\n",
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"# img = np.array(img.resize((299, 299))).reshape((1, 299, 299, 3))\n",
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"\n",
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"# scale the image and do other preprocessing\n",
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"img = img/255"
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"# # scale the image and do other preprocessing\n",
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"# img = img/255"
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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": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[2.87348394e-05, 3.31625670e-05, 2.48761153e-05, 2.91672295e-05,\n",
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" 1.58476214e-05, 2.04349963e-05, 2.29794769e-05, 2.98258401e-05,\n",
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" 7.79210823e-05, 1.14480768e-04, 4.18152449e-05, 5.98102342e-05,\n",
|
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" 8.04319134e-05, 1.35788669e-05, 3.69409063e-05, 2.70363416e-05,\n",
|
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" 4.31931585e-05, 2.60745201e-05, 5.94786252e-05, 4.99271664e-05,\n",
|
||||
" 2.43047725e-05, 1.30793895e-04, 4.14417882e-05, 2.70148976e-05,\n",
|
||||
" 4.10587854e-05, 6.58849458e-05, 3.18542261e-05, 2.28214885e-05,\n",
|
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" 4.62773096e-05, 2.48329743e-05, 2.42654005e-05, 2.44440125e-05,\n",
|
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" 4.15933137e-05, 2.09003556e-05, 1.59764550e-05, 1.34925976e-05,\n",
|
||||
" 5.11476246e-05, 3.04547921e-05, 5.38403292e-05, 2.52995142e-05,\n",
|
||||
" 2.60678062e-05, 1.34934933e-04, 5.17043627e-05, 1.98286180e-05,\n",
|
||||
" 1.71139363e-05, 1.33584617e-04, 3.85650528e-05, 4.20200013e-05,\n",
|
||||
" 4.11127767e-05, 4.81091338e-05, 2.95333430e-05, 1.35418804e-05,\n",
|
||||
" 2.56965777e-05, 3.57028839e-05, 3.47831883e-05, 1.86437192e-05,\n",
|
||||
" 2.68215954e-05, 3.62955325e-05, 3.25360379e-05, 2.10378585e-05,\n",
|
||||
" 5.16176551e-05, 1.74321522e-05, 1.70013354e-05, 3.05602764e-04,\n",
|
||||
" 2.08724887e-05, 4.20338292e-05, 3.24174725e-05, 5.40788969e-05,\n",
|
||||
" 3.05910835e-05, 3.09062816e-05, 3.70780981e-05, 5.52072815e-05,\n",
|
||||
" 5.70952288e-05, 9.20407110e-05, 7.28778541e-05, 7.14581111e-05,\n",
|
||||
" 1.53447272e-05, 8.25692987e-05, 1.66468253e-05, 3.68028523e-05,\n",
|
||||
" 3.37601887e-05, 6.55732729e-05, 7.85594093e-05, 1.06584033e-04,\n",
|
||||
" 2.29202069e-05, 8.09206249e-05, 9.53404306e-05, 3.45181325e-05,\n",
|
||||
" 4.02693950e-05, 4.56266353e-05, 2.36524411e-05, 3.45649423e-05,\n",
|
||||
" 2.90476983e-05, 4.14484311e-05, 6.00201092e-05, 2.55004470e-05,\n",
|
||||
" 2.67037540e-05, 4.19789467e-05, 1.82741569e-05, 3.19312676e-05,\n",
|
||||
" 2.70542678e-05, 5.47103627e-05, 3.11060867e-05, 1.23055879e-05,\n",
|
||||
" 2.46065429e-05, 1.89571019e-05, 6.07315133e-06, 2.28815934e-05,\n",
|
||||
" 2.02425854e-05, 4.30371547e-05, 4.66785350e-05, 2.23741263e-05,\n",
|
||||
" 5.11738763e-05, 1.96830188e-05, 6.12578588e-05, 3.12303055e-05,\n",
|
||||
" 2.31823124e-05, 3.49025431e-05, 4.09512031e-05, 3.99867422e-05,\n",
|
||||
" 2.12125287e-05, 4.49290274e-05, 4.11992005e-05, 2.76286501e-05,\n",
|
||||
" 5.41073750e-05, 2.77385552e-05, 1.73709468e-05, 7.04166450e-05,\n",
|
||||
" 4.19379321e-05, 1.01846208e-05, 5.60213484e-05, 1.86260368e-05,\n",
|
||||
" 2.41570415e-05, 2.32356997e-05, 3.61526509e-05, 3.48804824e-05,\n",
|
||||
" 3.82888429e-05, 1.91788040e-05, 2.66903371e-05, 2.22696453e-05,\n",
|
||||
" 8.82827590e-05, 5.88142175e-05, 4.24889695e-05, 1.39571493e-05,\n",
|
||||
" 2.34105919e-05, 9.14792054e-06, 2.38255616e-05, 2.36884007e-05,\n",
|
||||
" 4.16137882e-05, 2.61921778e-05, 9.35538628e-06, 4.06456202e-05,\n",
|
||||
" 5.00514470e-05, 5.93220975e-05, 4.06428662e-05, 4.57445349e-05,\n",
|
||||
" 9.33669071e-05, 6.87131251e-05, 6.56917036e-05, 1.63057150e-04,\n",
|
||||
" 2.07711382e-05, 3.40171828e-05, 5.25352189e-05, 7.87169774e-05,\n",
|
||||
" 2.05707147e-05, 2.78320695e-05, 6.96952338e-05, 5.21129659e-05,\n",
|
||||
" 7.22707264e-05, 1.92875741e-05, 2.23326842e-05, 1.34510556e-04,\n",
|
||||
" 2.18944042e-05, 2.92327531e-05, 3.70465386e-05, 5.92614815e-05,\n",
|
||||
" 1.72934742e-05, 1.91587696e-05, 2.92312779e-05, 2.92381355e-05,\n",
|
||||
" 3.74670744e-05, 1.88971571e-05, 8.49900607e-05, 4.99997332e-05,\n",
|
||||
" 1.66820264e-05, 7.92418869e-05, 7.79421171e-05, 3.04292171e-05,\n",
|
||||
" 1.57136732e-04, 7.85228622e-05, 7.21035758e-05, 1.09024957e-04,\n",
|
||||
" 4.36982591e-05, 2.41748094e-05, 6.00040876e-05, 2.53325179e-05,\n",
|
||||
" 1.18568069e-05, 2.16146182e-05, 1.14633331e-05, 2.29929574e-05,\n",
|
||||
" 4.85876917e-05, 2.81516332e-05, 5.11928120e-05, 2.46606432e-05,\n",
|
||||
" 2.56997391e-05, 3.24740686e-05, 6.63395913e-05, 5.39563443e-05,\n",
|
||||
" 7.46768637e-05, 1.44902097e-05, 6.21178260e-05, 7.94990920e-05,\n",
|
||||
" 5.26915173e-05, 2.44574185e-05, 4.84968186e-05, 5.58190695e-05,\n",
|
||||
" 6.10985153e-05, 3.27518101e-05, 4.99396410e-05, 4.66030106e-05,\n",
|
||||
" 4.85124510e-05, 4.44747529e-05, 8.11594291e-05, 5.17867738e-05,\n",
|
||||
" 3.27908747e-05, 5.72784738e-05, 3.77279248e-05, 2.79495016e-05,\n",
|
||||
" 1.14093185e-04, 3.22306660e-05, 6.37761405e-05, 4.70271916e-05,\n",
|
||||
" 5.12053120e-05, 3.55333796e-05, 4.20222823e-05, 7.67037345e-05,\n",
|
||||
" 6.28583803e-05, 5.06723591e-05, 1.65470137e-05, 8.55832404e-05,\n",
|
||||
" 9.10326271e-05, 4.44190991e-05, 2.66823718e-05, 4.90651146e-05,\n",
|
||||
" 3.16314836e-05, 1.70037019e-05, 2.22507952e-05, 3.08026210e-05,\n",
|
||||
" 2.92006262e-05, 5.21812508e-05, 2.88916035e-05, 3.42814019e-04,\n",
|
||||
" 3.08394483e-05, 3.21107982e-05, 5.83832407e-05, 3.17412378e-05,\n",
|
||||
" 4.86168028e-05, 3.69986228e-05, 7.35939539e-05, 1.01651509e-04,\n",
|
||||
" 6.33892123e-05, 1.16707757e-04, 4.40528347e-05, 7.76190718e-05,\n",
|
||||
" 4.07124644e-05, 3.60297745e-05, 3.41399682e-05, 3.80674792e-05,\n",
|
||||
" 2.62926351e-05, 6.99426528e-05, 4.08917695e-05, 6.30973009e-05,\n",
|
||||
" 1.36613016e-04, 6.11745490e-05, 4.07748266e-05, 4.84085140e-05,\n",
|
||||
" 3.05555557e-04, 1.16628311e-04, 6.46469707e-05, 8.43134148e-06,\n",
|
||||
" 5.46161064e-05, 2.23031002e-05, 6.53090974e-05, 2.36686647e-05,\n",
|
||||
" 1.82996901e-05, 1.40099131e-04, 5.82382090e-05, 4.51421191e-04,\n",
|
||||
" 2.13040854e-03, 4.35033289e-04, 5.41022397e-04, 1.60870492e-04,\n",
|
||||
" 4.68313883e-05, 9.54939544e-01, 3.10988798e-05, 3.05654467e-05,\n",
|
||||
" 3.23688837e-05, 2.17403522e-05, 3.35242439e-05, 5.57901185e-05,\n",
|
||||
" 3.60432459e-05, 2.85484166e-05, 3.54308868e-05, 2.04342177e-05,\n",
|
||||
" 5.79034349e-05, 8.77326820e-05, 5.87764771e-05, 7.48893217e-05,\n",
|
||||
" 3.75710188e-05, 6.65013722e-05, 3.93841146e-05, 4.30815053e-05,\n",
|
||||
" 3.90250025e-05, 2.49465302e-05, 6.32623924e-05, 2.82751062e-05,\n",
|
||||
" 4.39944779e-05, 2.91896849e-05, 6.09816307e-05, 3.73154508e-05,\n",
|
||||
" 2.32816583e-05, 3.47097885e-05, 2.34287490e-05, 3.12690463e-05,\n",
|
||||
" 3.78116711e-05, 2.44250441e-05, 5.76958992e-05, 1.89158709e-05,\n",
|
||||
" 3.32920463e-05, 7.67280217e-05, 3.95814968e-05, 1.52310495e-05,\n",
|
||||
" 2.03246800e-05, 2.73730711e-05, 1.54649297e-05, 4.58700415e-05,\n",
|
||||
" 9.81626581e-05, 1.53429410e-05, 1.71078354e-05, 2.43297018e-05,\n",
|
||||
" 7.51380576e-05, 1.96585552e-05, 1.77083912e-05, 3.09253592e-05,\n",
|
||||
" 2.97734059e-05, 2.21605824e-05, 2.90767166e-05, 1.85748777e-05,\n",
|
||||
" 4.97450383e-05, 1.88307531e-05, 1.19116285e-05, 7.33295456e-05,\n",
|
||||
" 6.87958745e-05, 3.93158378e-04, 2.31156846e-05, 2.53631715e-05,\n",
|
||||
" 1.65625333e-05, 1.65534693e-05, 4.20270917e-05, 3.63402833e-05,\n",
|
||||
" 4.49393556e-05, 2.52205973e-05, 3.12724478e-05, 2.30503265e-05,\n",
|
||||
" 2.50303183e-05, 7.02027774e-06, 3.39574144e-05, 2.14463907e-05,\n",
|
||||
" 6.73558243e-05, 2.93099947e-05, 4.04911734e-05, 1.33556852e-04,\n",
|
||||
" 3.94189883e-05, 9.10259332e-05, 3.55042503e-05, 1.96132933e-05,\n",
|
||||
" 2.23557199e-05, 1.78663686e-05, 2.76167684e-05, 2.52928312e-05,\n",
|
||||
" 2.53802773e-05, 1.75146706e-05, 3.04427449e-05, 3.05658868e-05,\n",
|
||||
" 2.93310241e-05, 3.47113091e-05, 3.17042395e-05, 4.88488004e-05,\n",
|
||||
" 5.35136824e-05, 1.63490895e-05, 3.07410519e-05, 2.58094551e-05,\n",
|
||||
" 1.68334973e-05, 2.69737084e-05, 1.76451395e-05, 8.72271194e-05,\n",
|
||||
" 4.76380628e-05, 6.58142962e-05, 4.99944399e-05, 3.69260088e-05,\n",
|
||||
" 1.29387572e-05, 4.35098846e-05, 3.55075354e-05, 5.56325867e-05,\n",
|
||||
" 3.39463732e-05, 4.18462005e-05, 2.19590602e-05, 2.19191188e-05,\n",
|
||||
" 1.31938330e-04, 4.50956250e-05, 5.07826589e-05, 4.17550691e-05,\n",
|
||||
" 7.16164577e-05, 3.72835784e-05, 2.07126468e-05, 4.95999884e-05,\n",
|
||||
" 2.19804733e-05, 3.38278987e-05, 3.40998122e-05, 2.76599694e-05,\n",
|
||||
" 5.85454873e-05, 2.18334353e-05, 3.34332472e-05, 4.01523976e-05,\n",
|
||||
" 4.58525028e-05, 3.80572783e-05, 1.40437096e-05, 2.83117733e-05,\n",
|
||||
" 4.42011333e-05, 1.78016035e-05, 1.94806598e-05, 1.90080063e-05,\n",
|
||||
" 6.83374310e-05, 3.57353092e-05, 2.33811606e-05, 2.66535317e-05,\n",
|
||||
" 5.60822373e-05, 2.42737551e-05, 2.61371079e-05, 2.67774813e-05,\n",
|
||||
" 1.90776718e-05, 1.29542277e-05, 2.81340526e-05, 1.97947316e-04,\n",
|
||||
" 4.21368532e-05, 2.00126105e-05, 3.13872697e-05, 5.18013985e-05,\n",
|
||||
" 4.06897962e-05, 5.17138833e-05, 1.88349750e-05, 4.25781291e-05,\n",
|
||||
" 2.22635099e-05, 2.73323512e-05, 3.70964553e-05, 1.27892481e-05,\n",
|
||||
" 2.74279973e-05, 9.07500435e-05, 4.62964235e-05, 2.69962766e-05,\n",
|
||||
" 2.29480265e-05, 3.62087230e-05, 9.92782880e-05, 7.58480746e-05,\n",
|
||||
" 2.73580899e-05, 3.78854857e-05, 1.52056227e-05, 4.57414790e-05,\n",
|
||||
" 2.05655360e-05, 3.76061143e-05, 4.05851133e-05, 4.74572698e-05,\n",
|
||||
" 5.02792682e-05, 2.73430123e-05, 1.88959839e-05, 3.63449617e-05,\n",
|
||||
" 8.58449785e-05, 4.83370568e-05, 2.29676662e-05, 1.39783297e-05,\n",
|
||||
" 3.00153624e-05, 9.30200713e-06, 2.98171090e-05, 4.02061924e-05,\n",
|
||||
" 4.37480085e-05, 2.40602913e-05, 3.97164113e-05, 3.02438275e-05,\n",
|
||||
" 3.59209807e-05, 2.26508218e-05, 8.11787104e-05, 4.59786570e-05,\n",
|
||||
" 3.35251389e-05, 4.65675585e-05, 7.03359765e-05, 3.02757162e-05,\n",
|
||||
" 1.55114576e-05, 2.08013098e-05, 3.21109837e-05, 3.39043763e-05,\n",
|
||||
" 3.69621230e-05, 4.77702197e-05, 2.04758471e-05, 4.22765661e-05,\n",
|
||||
" 1.94308050e-05, 7.30149040e-05, 1.79527942e-05, 2.80337772e-05,\n",
|
||||
" 4.05040737e-05, 3.21173502e-05, 3.44566943e-05, 4.53288958e-05,\n",
|
||||
" 1.00638936e-05, 9.34529671e-05, 3.09277821e-05, 2.75657203e-05,\n",
|
||||
" 7.13371846e-05, 4.78991387e-05, 1.97249592e-05, 2.87434432e-05,\n",
|
||||
" 3.84232007e-05, 3.48397916e-05, 6.53180105e-05, 4.45334517e-05,\n",
|
||||
" 4.27828672e-05, 2.16301651e-05, 6.91576715e-05, 7.24355050e-05,\n",
|
||||
" 1.77990405e-05, 2.19066005e-05, 4.36122064e-05, 1.95597968e-05,\n",
|
||||
" 1.43805864e-05, 3.36158046e-05, 3.29781979e-05, 2.36990436e-05,\n",
|
||||
" 3.80291931e-05, 2.94673118e-05, 5.39072244e-05, 1.34538832e-05,\n",
|
||||
" 4.52095228e-05, 2.56473413e-05, 4.88241676e-05, 3.79534722e-05,\n",
|
||||
" 9.00277664e-05, 2.71920580e-05, 1.99786864e-05, 4.11888686e-05,\n",
|
||||
" 2.08715737e-05, 3.21811240e-05, 3.77245779e-05, 3.90776258e-05,\n",
|
||||
" 5.72720819e-05, 2.28592144e-05, 8.38642154e-05, 7.11378598e-05,\n",
|
||||
" 7.47661543e-05, 2.76085266e-05, 2.37599397e-05, 2.74038648e-05,\n",
|
||||
" 1.96829624e-05, 2.75633538e-05, 1.81687465e-05, 1.65650599e-05,\n",
|
||||
" 1.66132322e-05, 1.77653765e-05, 1.31694123e-05, 4.40269687e-05,\n",
|
||||
" 5.24848438e-05, 7.23133689e-06, 6.08247465e-05, 4.91629107e-05,\n",
|
||||
" 2.79622727e-05, 3.83454862e-05, 3.14143108e-05, 7.62736527e-05,\n",
|
||||
" 7.78079848e-05, 3.67796965e-05, 2.58853524e-05, 2.09938262e-05,\n",
|
||||
" 6.02522668e-05, 2.05882207e-05, 5.41717600e-05, 2.36419546e-05,\n",
|
||||
" 2.47464232e-05, 2.56587409e-05, 4.29635875e-05, 2.33233714e-05,\n",
|
||||
" 2.72860962e-05, 1.83634984e-05, 3.15190737e-05, 3.37215424e-05,\n",
|
||||
" 4.02502737e-05, 4.96676294e-05, 3.87466462e-05, 4.78446273e-05,\n",
|
||||
" 1.86023553e-05, 1.04718667e-04, 3.17602207e-05, 9.92937275e-05,\n",
|
||||
" 2.01554867e-04, 3.02287735e-05, 3.60458944e-05, 2.53110029e-05,\n",
|
||||
" 2.71016797e-05, 4.33302957e-05, 1.72166110e-05, 2.25411804e-05,\n",
|
||||
" 2.48068172e-05, 5.14635103e-05, 1.58837247e-05, 1.93799478e-05,\n",
|
||||
" 1.45588992e-05, 4.06311265e-05, 9.53586550e-06, 4.33395144e-05,\n",
|
||||
" 6.12365402e-05, 4.95142558e-05, 5.42290290e-05, 3.97067161e-05,\n",
|
||||
" 9.20145976e-06, 5.19714195e-05, 5.91691532e-05, 7.11168977e-05,\n",
|
||||
" 1.74283286e-05, 1.11089117e-04, 3.03591587e-05, 6.13862794e-05,\n",
|
||||
" 4.03964805e-05, 3.48730646e-05, 4.12873851e-05, 3.63344952e-05,\n",
|
||||
" 9.57763186e-05, 5.08481317e-05, 2.18448913e-05, 2.55160630e-05,\n",
|
||||
" 2.60871548e-05, 2.94701222e-05, 3.19013780e-05, 4.27702980e-05,\n",
|
||||
" 3.36178891e-05, 5.91083517e-05, 4.76461501e-05, 2.87710882e-05,\n",
|
||||
" 7.71013802e-05, 3.33449207e-05, 3.10998585e-05, 2.31554441e-05,\n",
|
||||
" 7.88360558e-05, 4.88579790e-05, 4.79332739e-05, 6.66515261e-05,\n",
|
||||
" 2.80267468e-05, 3.95161696e-05, 2.19156900e-05, 4.22459379e-05,\n",
|
||||
" 2.90575063e-05, 3.46283523e-05, 4.19461721e-05, 4.79287955e-05,\n",
|
||||
" 4.88870210e-05, 6.30793729e-05, 6.10515781e-05, 7.35698122e-05,\n",
|
||||
" 4.14108945e-05, 2.09264635e-05, 2.75761595e-05, 2.45826413e-05,\n",
|
||||
" 1.17834075e-04, 2.33378105e-05, 2.12311697e-05, 2.49118675e-05,\n",
|
||||
" 1.44527812e-05, 7.47653685e-05, 2.85598799e-05, 4.30836189e-06,\n",
|
||||
" 4.16856419e-05, 6.70859372e-05, 2.27318233e-05, 2.34566724e-05,\n",
|
||||
" 2.86041468e-05, 3.12322700e-05, 4.20835640e-05, 2.88782139e-05,\n",
|
||||
" 2.12479808e-05, 2.67499399e-05, 4.26801307e-05, 3.50373411e-05,\n",
|
||||
" 1.14289542e-04, 2.39087785e-05, 2.87024377e-05, 5.50144468e-05,\n",
|
||||
" 1.25675524e-05, 8.60681976e-05, 5.79822372e-05, 2.25609238e-05,\n",
|
||||
" 1.58922521e-05, 5.59906366e-05, 5.47513882e-05, 3.99525888e-05,\n",
|
||||
" 4.46637750e-05, 3.12694647e-05, 4.69786464e-05, 7.30282045e-05,\n",
|
||||
" 3.32598356e-05, 3.93198643e-05, 2.98816431e-05, 4.68274120e-05,\n",
|
||||
" 3.85413005e-05, 2.63213096e-05, 7.29164458e-05, 1.51059212e-05,\n",
|
||||
" 1.73022017e-05, 2.24817995e-05, 2.50870999e-05, 2.70464498e-05,\n",
|
||||
" 6.94527189e-05, 7.03223559e-05, 1.13023976e-04, 1.81350424e-05,\n",
|
||||
" 1.61756379e-05, 2.27133587e-05, 1.73909539e-05, 3.29188697e-05,\n",
|
||||
" 3.83688603e-05, 4.63621691e-05, 4.20097022e-05, 2.44141211e-05,\n",
|
||||
" 2.36812193e-05, 5.37081723e-05, 3.25651235e-05, 6.26961337e-05,\n",
|
||||
" 4.40348194e-05, 6.08678674e-05, 1.51382401e-05, 4.85360542e-05,\n",
|
||||
" 2.52648915e-05, 2.13416624e-05, 3.66176173e-05, 2.14315878e-05,\n",
|
||||
" 2.53550206e-05, 2.49690220e-05, 1.72549426e-05, 1.23161544e-05,\n",
|
||||
" 4.05697538e-05, 2.80514014e-05, 4.10169851e-05, 1.82738422e-05,\n",
|
||||
" 2.12066843e-05, 1.92876123e-05, 1.40940101e-05, 2.85765000e-05,\n",
|
||||
" 1.49180614e-05, 2.17154247e-05, 1.15241521e-04, 6.20267747e-05,\n",
|
||||
" 3.51752824e-05, 1.84352139e-05, 2.34445524e-05, 6.70253139e-05,\n",
|
||||
" 3.65042324e-05, 3.79433368e-05, 4.84678712e-05, 3.16102814e-05,\n",
|
||||
" 4.14562965e-05, 3.48020985e-05, 5.51545527e-05, 1.20085324e-05,\n",
|
||||
" 4.02397964e-05, 3.69577174e-05, 1.16221108e-05, 1.73307726e-05,\n",
|
||||
" 2.36364995e-05, 3.68570509e-05, 2.28376211e-05, 1.44234800e-05,\n",
|
||||
" 4.29613319e-05, 2.90063417e-05, 3.12782940e-05, 3.48059839e-05,\n",
|
||||
" 3.07464470e-05, 4.45889127e-05, 2.77584841e-05, 2.82693636e-05,\n",
|
||||
" 3.06526708e-05, 2.07262001e-05, 2.39080709e-05, 3.90869063e-05,\n",
|
||||
" 1.63033437e-05, 3.13781420e-05, 1.09535986e-05, 2.71046247e-05,\n",
|
||||
" 1.11349189e-04, 5.54282742e-05, 1.40334141e-05, 4.46840531e-05,\n",
|
||||
" 3.27371636e-05, 2.36247342e-05, 4.71842868e-05, 2.51329329e-05,\n",
|
||||
" 1.72788041e-05, 4.25959151e-05, 3.08176059e-05, 2.34919771e-05,\n",
|
||||
" 5.41002009e-05, 3.36215126e-05, 1.79904982e-05, 2.53810031e-05,\n",
|
||||
" 2.60098714e-05, 3.03672950e-05, 3.66435743e-05, 1.58947860e-05,\n",
|
||||
" 1.63827226e-05, 1.00799487e-04, 7.84313306e-05, 3.44231594e-05,\n",
|
||||
" 2.11487786e-05, 2.59042172e-05, 2.38122284e-05, 4.10612520e-05,\n",
|
||||
" 7.02113830e-05, 5.71030141e-05, 3.37046913e-05, 5.70804186e-05,\n",
|
||||
" 4.23736856e-05, 5.22688570e-05, 1.19306824e-05, 5.73656653e-05,\n",
|
||||
" 7.22504701e-05, 3.07254595e-05, 1.82885069e-05, 3.95821407e-05,\n",
|
||||
" 3.25709625e-05, 3.28924471e-05, 9.97101160e-05, 2.37495660e-05,\n",
|
||||
" 4.08896231e-05, 5.16752771e-05, 2.26338507e-05, 3.62301726e-05,\n",
|
||||
" 3.16428268e-05, 3.80293750e-05, 2.15715372e-05, 4.88352052e-05,\n",
|
||||
" 5.38927270e-05, 1.52157181e-05, 3.10339638e-05, 7.30824031e-05,\n",
|
||||
" 5.28455093e-05, 7.30705578e-05, 5.31096957e-05, 2.09516438e-05,\n",
|
||||
" 2.92397508e-05, 1.90421888e-05, 2.24708656e-05, 5.41521295e-05,\n",
|
||||
" 4.26290353e-05, 2.68298045e-05, 1.68493905e-04, 7.92833162e-05,\n",
|
||||
" 2.62088943e-05, 3.02322060e-05, 4.04044986e-05, 3.22642190e-05,\n",
|
||||
" 3.49984402e-05, 6.04081906e-05, 3.70786656e-05, 3.49358452e-05,\n",
|
||||
" 5.57010717e-05, 6.00058593e-05, 2.44417952e-05, 5.71263263e-05,\n",
|
||||
" 3.27215894e-05, 3.40137776e-05, 1.18062126e-05, 6.24499153e-05,\n",
|
||||
" 6.80528974e-05, 5.41649897e-05, 2.79301566e-05, 6.44374522e-05,\n",
|
||||
" 2.42756778e-05, 1.69049254e-05, 3.07581759e-05, 3.14246172e-05,\n",
|
||||
" 4.06918080e-05, 1.29262517e-05, 3.18938037e-05, 2.57563679e-05,\n",
|
||||
" 4.66018973e-05, 5.63653448e-05, 3.38337704e-05, 2.77759864e-05,\n",
|
||||
" 2.10913349e-05, 2.39067704e-05, 5.60720728e-05, 3.24895882e-05,\n",
|
||||
" 3.95797579e-05, 8.02239229e-05, 1.01568221e-05, 2.71663976e-05,\n",
|
||||
" 4.02033183e-05, 3.46393863e-05, 1.89300026e-05, 6.62385719e-05,\n",
|
||||
" 2.61362366e-05, 1.68800689e-05, 4.81760289e-05, 2.70115488e-05,\n",
|
||||
" 6.33779127e-05, 9.80817131e-05, 1.00358353e-04, 1.55335729e-05,\n",
|
||||
" 6.72744281e-05, 2.67282903e-05, 5.28051060e-05, 2.69585871e-05,\n",
|
||||
" 4.51873238e-05, 1.50139886e-05, 4.31409971e-05, 2.19486756e-05,\n",
|
||||
" 2.87472831e-05, 2.18212153e-05, 5.95341808e-05, 4.36046794e-05,\n",
|
||||
" 2.39972505e-05, 2.03701456e-05, 2.67976375e-05, 1.95541270e-05,\n",
|
||||
" 4.66474739e-05, 3.60696904e-05, 1.68149654e-05, 2.32476013e-05,\n",
|
||||
" 2.53057151e-05, 3.79400080e-05, 4.50154475e-05, 4.20643009e-05,\n",
|
||||
" 2.56692183e-05, 3.46892048e-05, 3.57301287e-05, 3.69169247e-05,\n",
|
||||
" 3.51629387e-05, 3.76432145e-05, 2.19416434e-05, 2.46851632e-05,\n",
|
||||
" 6.47292763e-05, 4.90587045e-05, 9.84386497e-05, 4.41858792e-05,\n",
|
||||
" 1.67457674e-05, 7.27501538e-05, 1.29627551e-05, 4.09351560e-05,\n",
|
||||
" 4.05014071e-05, 5.26341646e-05, 3.61480306e-05, 4.60584415e-05,\n",
|
||||
" 6.60331425e-05, 2.30091900e-05, 4.62839744e-05, 2.25598706e-05,\n",
|
||||
" 3.43657230e-05, 4.64396071e-05, 4.21469849e-05, 4.19451717e-05,\n",
|
||||
" 1.95988596e-05, 2.81598495e-05, 2.10212929e-05, 2.90198714e-05,\n",
|
||||
" 3.15838552e-05, 1.42506296e-05, 1.96369892e-05, 1.41922828e-05,\n",
|
||||
" 2.51494548e-05, 5.29627650e-05, 2.34803319e-05, 2.45093706e-05,\n",
|
||||
" 5.32276354e-05, 3.60567574e-05, 3.34151773e-05, 4.46611339e-05,\n",
|
||||
" 1.84819692e-05, 3.09412899e-05, 4.80864292e-05, 4.70178165e-05,\n",
|
||||
" 7.63339776e-05, 4.71588719e-05, 4.23062920e-05, 4.86267672e-05,\n",
|
||||
" 2.40010959e-05, 1.92821499e-05, 1.88615959e-05, 5.05874268e-05,\n",
|
||||
" 4.82907526e-05, 3.97411168e-05, 5.72696772e-05, 2.60267680e-05,\n",
|
||||
" 5.70021366e-05, 1.14346831e-05, 5.19260393e-05, 6.01843822e-05]],\n",
|
||||
" dtype=float32)"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model.predict(img)"
|
||||
"# model.predict(img)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -343,10 +82,10 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"inp = gradio.inputs.ImageUpload()\n",
|
||||
"out = gradio.outputs.Label(label_names='imagenet1000')\n",
|
||||
"out = gradio.outputs.Label(label_names='imagenet1000', max_label_length=8)\n",
|
||||
"\n",
|
||||
"io = gradio.Interface(inputs=inp, \n",
|
||||
" outputs=\"label\",\n",
|
||||
" outputs=out,\n",
|
||||
" model=model, \n",
|
||||
" model_type='keras')"
|
||||
]
|
||||
@ -362,9 +101,9 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"NOTE: Gradio is in beta stage, please report all bugs to: a12d@stanford.edu\n",
|
||||
"NOTE: Gradio is in beta stage, please report all bugs to: contact.gradio@gmail.com\n",
|
||||
"Model is running locally at: http://localhost:7860/\n",
|
||||
"Model available publicly for 8 hours at: https://d2f02315.gradio.app/\n"
|
||||
"Model available publicly for 8 hours at: https://2140c179.gradio.app/\n"
|
||||
]
|
||||
},
|
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{
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@ -381,7 +120,7 @@
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" "
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],
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"text/plain": [
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"<IPython.lib.display.IFrame at 0x2837759ea90>"
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"<IPython.lib.display.IFrame at 0x1dc825539e8>"
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]
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||||
},
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"metadata": {},
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@ -5,6 +5,10 @@
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||||
<link href="../style/style.css" rel="stylesheet">
|
||||
<link href="../style/blog.css" rel="stylesheet">
|
||||
<link href="../style/gradio.css" rel="stylesheet">
|
||||
<link rel="stylesheet"
|
||||
href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.15.6/styles/github.min.css">
|
||||
<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.15.6/highlight.min.js"></script>
|
||||
<script>hljs.initHighlightingOnLoad();</script>
|
||||
</head>
|
||||
<body>
|
||||
<nav>
|
||||
@ -18,9 +22,9 @@
|
||||
<div class="row">
|
||||
<div class="leftcolumn">
|
||||
<div class="card">
|
||||
<h2>Beginner's Tutorial to Gradio: Creating a Sketchpad for a Keras MNIST Model</h2>
|
||||
<h2>Beginner's Tutorial: Creating a Sketchpad for a Keras MNIST Model</h2>
|
||||
<h4>Abubakar Abid, April 9, 2019</h4>
|
||||
<div class="fakeimg" style="height:200px;">Image</div>
|
||||
<img src="../img/mnist-sketchpad-screenshot.png"></img>
|
||||
<p>Gradio is a python library that makes it easy to turn your machine learning models into visual interfaces!
|
||||
This tutorial shows you how to do that with the "Hello World" of machine learning models: a model that we train
|
||||
from scratch to classify hand-written digits on the MNIST dataset. By the end, you will create an interface
|
||||
@ -29,24 +33,54 @@
|
||||
<a href="https://colab.research.google.com/drive/1DQSuxGARUZ-v4ZOAuw-Hf-8zqegpmes-">Check out the colab notebook here.</a></p>
|
||||
<h3>Installing Gradio</h3>
|
||||
<p>If you haven't already installed gradio, go ahead and do so. It's super easy as long as you have Python3 already on your machine:</p>
|
||||
<pre><code class="bash">pip install gradio</code></pre>
|
||||
<h3>The MNIST Dataset</h3>
|
||||
<p>The MNIST dataset consists of images of handwritten digits. We'll be training a model to classify the image
|
||||
into the digit written, from 0 through 9. Here is a sample of handwritten digits.</p>
|
||||
into the digit written, from 0 through 9, so let's load the data.
|
||||
|
||||
<pre><code class="python">import tensorflow as tf
|
||||
(x_train, y_train),(x_test, y_test) = tf.keras.datasets.mnist.load_data()
|
||||
x_train, x_test = x_train / 255.0, x_test / 255.0</code></pre>
|
||||
|
||||
Here is a sample of handwritten digits.</p>
|
||||
<img src="../img/mnist-examples.jpg"></img>
|
||||
<h3>Training a Keras Model</h3>
|
||||
<p>By using the keras API from the tensorflow package, we can train a model in just a few lines of code. Here,
|
||||
we're not going to train a very complicated model -- it'll just be a fully connected neural network. Since we're
|
||||
not really going for record accuracies, let's just train it only for 5 epochs.</p>
|
||||
|
||||
<pre><code class="python">model = tf.keras.models.Sequential([
|
||||
tf.keras.layers.Flatten(),
|
||||
tf.keras.layers.Dense(512, activation=tf.nn.relu),
|
||||
tf.keras.layers.Dropout(0.2),
|
||||
tf.keras.layers.Dense(10, activation=tf.nn.softmax)
|
||||
])
|
||||
|
||||
model.compile(optimizer='adam',
|
||||
loss='sparse_categorical_crossentropy',
|
||||
metrics=['accuracy'])
|
||||
model.fit(x_train, y_train, epochs=1)</code></pre>
|
||||
|
||||
<h3>Launching a Gradio Interface</h3>
|
||||
<p>Now that we have our keras model trained, we'll want to actually define the interface. What's the appropriate
|
||||
interface to use? For the input, we can use a sketchpad, so that users can use their cursor to create new </p>
|
||||
interface to use? For the input, we can use a Sketchpad, so that users can use their cursor to create new digits and
|
||||
test the model (we call this process <em>interactive inference</em>). The output of the model is simply a label,
|
||||
so we will use the Label interface. </p>
|
||||
|
||||
<pre><code class="python">io = gradio.Interface(
|
||||
inputs="sketchpad",
|
||||
outputs="label",
|
||||
model=model,
|
||||
model_type='keras')
|
||||
|
||||
io.launch(inline=True, share=False)
|
||||
</code></pre>
|
||||
And that's it. Try it out <a href="https://colab.research.google.com/drive/1DQSuxGARUZ-v4ZOAuw-Hf-8zqegpmes-">in the colab notebook here.</a>
|
||||
You'll notice that the interface is embedded directly in the colab notebook!</p>
|
||||
|
||||
</div>
|
||||
<!--<div class="card">-->
|
||||
<!--<h2>TITLE HEADING</h2>-->
|
||||
<!--<h5>Title description, Sep 2, 2017</h5>-->
|
||||
<!--<div class="fakeimg" style="height:200px;">Image</div>-->
|
||||
<!--<p>Some text..</p>-->
|
||||
<!--</div>-->
|
||||
<!--</div>-->
|
||||
|
||||
|
||||
</div>
|
||||
</div>
|
||||
<footer>
|
||||
|
||||
@ -18,22 +18,22 @@
|
||||
<div class="row">
|
||||
<div class="leftcolumn">
|
||||
<div class="card">
|
||||
<h2><a href="articles/beginners-tutorial-gradio-mnist-keras.html">Beginner's Tutorial to Gradio: Creating a Sketchpad for a Keras MNIST Model</a></h2>
|
||||
<h2><a href="articles/beginners-tutorial-gradio-mnist-keras.html">Beginner's Tutorial: Creating a Sketchpad for a Keras MNIST Model</a></h2>
|
||||
<h4>Abubakar Abid, April 9, 2019</h4>
|
||||
<div class="fakeimg" style="height:200px;">Image</div>
|
||||
<img src="img/mnist-sketchpad-screenshot.png"></img>
|
||||
<p>Gradio is a python library that makes it easy to turn your machine learning models into visual interfaces!
|
||||
This tutorial shows you how to do that with the "Hello World" of machine learning models: a model that we train
|
||||
from scratch to classify hand-written digits on the MNIST dataset. By the end, you will create an interface
|
||||
that allows you to draw handwritten digits and see the results of the classifier.
|
||||
that allows you to draw handwritten digits and see the results of the classifier in your browser.
|
||||
<a href="articles/beginners-tutorial-gradio-mnist-keras.html">Read more >>.</a></p>
|
||||
</div>
|
||||
<div class="card">
|
||||
<h2>Why Machine Learning Accessibility is Important</h2>
|
||||
<h2>Gradio: A Way to Improve Machine Learning Accessibility</h2>
|
||||
<h4>Gradio Team, April 8, 2019</h4>
|
||||
<div class="fakeimg" style="height:200px;">Image</div>
|
||||
<img src="img/biased-google-translate.png"></img>
|
||||
<p>Why did we create gradio? In this article, we describe the problems we have experienced with machine learning
|
||||
models and why we think gradio can help solve them.
|
||||
<a href="articles/beginners-tutorial-gradio-mnist-keras.html">Read more >>.</a></p>
|
||||
models, in particular that of <em>accessibility</em>, and why we think gradio can help solve them.
|
||||
<a href="articles/beginners-tutorial-gradio-mnist-keras.html">Coming soon.</a></p>
|
||||
</div> </div>
|
||||
</div>
|
||||
<footer>
|
||||
|
||||
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color: #ed9013;
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font-weight: bold;
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Reference in New Issue
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