gradio/Test Keras MNIST.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import gradio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train, y_train),(x_test, y_test) = tf.keras.datasets.mnist.load_data()\n",
    "x_train, x_test = x_train / 255.0, x_test / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.models.Sequential([\n",
    "  tf.keras.layers.Flatten(),\n",
    "  tf.keras.layers.Dense(512, activation=tf.nn.relu),\n",
    "  tf.keras.layers.Dropout(0.2),\n",
    "  tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n",
    "])\n",
    "\n",
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1\n",
      "60000/60000 [==============================] - 25s 417us/step - loss: 0.2210 - acc: 0.9351\n"
     ]
    },
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     "execution_count": 4,
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   "source": [
    "model.fit(x_train, y_train, epochs=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "iface = gradio.Interface(inputs=\"sketchpad\", outputs=\"label\", model=model, model_type='keras')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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     "name": "stdout",
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     "text": [
      "No validation samples for this interface... skipping validation.\n",
      "NOTE: Gradio is in beta stage, please report all bugs to: contact.gradio@gmail.com\n",
      "Model is running locally at: http://localhost:7861/\n",
      "To create a public link, set `share=True` in the argument to `launch()`\n"
     ]
    },
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     "execution_count": 6,
     "metadata": {},
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   "source": [
    "iface.launch(inline=True, share=False)"
   ]
  }
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