From ca3d080e079c8048dfa70ead94249ab40a935586 Mon Sep 17 00:00:00 2001 From: Abubakar Abid Date: Sat, 30 Mar 2019 12:43:06 -0700 Subject: [PATCH] added jquery and a few more test notebooks --- Test Pytorch.ipynb | 746 ++++++++++++++++++++++ Test Sklearn.ipynb | 211 ++++++ Test Tensorflow.ipynb | 196 ++++++ gradio/static/js/jquery-3.2.1.slim.min.js | 4 + 4 files changed, 1157 insertions(+) create mode 100644 Test Pytorch.ipynb create mode 100644 Test Sklearn.ipynb create mode 100644 Test Tensorflow.ipynb create mode 100644 gradio/static/js/jquery-3.2.1.slim.min.js diff --git a/Test Pytorch.ipynb b/Test Pytorch.ipynb new file mode 100644 index 0000000000..b96f9f06c9 --- /dev/null +++ b/Test Pytorch.ipynb @@ -0,0 +1,746 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import torch\n", + "import torch.nn as nn\n", + "import torchvision\n", + "import torchvision.transforms as transforms\n", + "import gradio" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Device configuration\n", + "device = torch.device('cpu')\n", + "\n", + "# Hyper-parameters \n", + "input_size = 784\n", + "hidden_size = 500\n", + "num_classes = 10\n", + "num_epochs = 2\n", + "batch_size = 100\n", + "learning_rate = 0.001\n", + "\n", + "# MNIST dataset \n", + "train_dataset = torchvision.datasets.MNIST(root='../../data', train=True, transform=transforms.ToTensor(), download=True)\n", + "test_dataset = torchvision.datasets.MNIST(root='../../data',train=False, transform=transforms.ToTensor())\n", + "train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size,shuffle=True)\n", + "test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [1/2], Step [100/600], Loss: 0.4317\n", + "Epoch [1/2], Step [200/600], Loss: 0.2267\n", + "Epoch [1/2], Step [300/600], Loss: 0.2052\n", + "Epoch [1/2], Step [400/600], Loss: 0.1179\n", + "Epoch [1/2], Step [500/600], Loss: 0.1108\n", + "Epoch [1/2], Step [600/600], Loss: 0.1830\n", + "Epoch [2/2], Step [100/600], Loss: 0.0972\n", + "Epoch [2/2], Step [200/600], Loss: 0.0662\n", + "Epoch [2/2], Step [300/600], Loss: 0.1487\n", + "Epoch [2/2], Step [400/600], Loss: 0.0640\n", + "Epoch [2/2], Step [500/600], Loss: 0.0425\n", + "Epoch [2/2], Step [600/600], Loss: 0.0979\n" + ] + } + ], + "source": [ + "# Fully connected neural network with one hidden layer\n", + "class NeuralNet(nn.Module):\n", + " def __init__(self, input_size, hidden_size, num_classes):\n", + " super(NeuralNet, self).__init__()\n", + " self.fc1 = nn.Linear(input_size, hidden_size) \n", + " self.relu = nn.ReLU()\n", + " self.fc2 = nn.Linear(hidden_size, num_classes) \n", + " \n", + " def forward(self, x):\n", + " out = self.fc1(x)\n", + " out = self.relu(out)\n", + " out = self.fc2(out)\n", + " return out\n", + "\n", + "model = NeuralNet(input_size, hidden_size, num_classes).to(device)\n", + "\n", + "# Loss and optimizer\n", + "criterion = nn.CrossEntropyLoss()\n", + "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate) \n", + "\n", + "# Train the model\n", + "total_step = len(train_loader)\n", + "for epoch in range(num_epochs):\n", + " for i, (images, labels) in enumerate(train_loader): \n", + " # Move tensors to the configured device\n", + " images = images.reshape(-1, 28*28).to(device)\n", + " labels = labels.to(device)\n", + " \n", + " # Forward pass\n", + " outputs = model(images)\n", + " loss = criterion(outputs, labels)\n", + " \n", + " # Backward and optimize\n", + " optimizer.zero_grad()\n", + " loss.backward()\n", + " optimizer.step()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy of the network on the 10000 test images: 97.04 %\n" + ] + } + ], + "source": [ + "# Test the model\n", + "# In test phase, we don't need to compute gradients (for memory efficiency)\n", + "with torch.no_grad():\n", + " correct = 0\n", + " total = 0\n", + " for images, labels in test_loader:\n", + " images = images.reshape(-1, 28*28).to(device)\n", + " labels = labels.to(device)\n", + " outputs = model(images)\n", + " _, predicted = torch.max(outputs.data, 1)\n", + " total += labels.size(0)\n", + " correct += (predicted == labels).sum().item()\n", + "\n", + " print('Accuracy of the network on the 10000 test images: {} %'.format(100 * correct / total))" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.float64\n", + "torch.float64\n" + ] + }, + { + "ename": "RuntimeError", + "evalue": "Expected object of type torch.FloatTensor but found type torch.DoubleTensor for argument #4 'mat1'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mVariable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mprediction\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m 475\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 476\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 477\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m 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\u001b[1;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlinear\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 56\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 57\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mextra_repr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\nn\\functional.py\u001b[0m in \u001b[0;36mlinear\u001b[1;34m(input, weight, bias)\u001b[0m\n\u001b[0;32m 1022\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m2\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mbias\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1023\u001b[0m \u001b[1;31m# fused op is marginally faster\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1024\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maddmm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbias\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1025\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1026\u001b[0m \u001b[0moutput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmatmul\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mweight\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mRuntimeError\u001b[0m: Expected object of type torch.FloatTensor but found type torch.DoubleTensor for argument #4 'mat1'" + ] + } + ], + "source": [ + "value = torch.from_numpy(images.numpy())\n", + "print(value.dtype)\n", + "value = torch.autograd.Variable(value)\n", + "print(value.dtype)\n", + "prediction = model(value)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dtype('float64')" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "images.numpy().astype('float64').dtype" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(100, 10)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prediction.data.numpy().shape" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-2.94313431e+00, -1.81460023e+00, -2.08448991e-01,\n", + " -2.29123878e+00, -2.91417217e+00, -7.30904102e-01,\n", + " -1.85286796e+00, -8.89607048e+00, 3.85826755e+00,\n", + " -5.70444298e+00],\n", + " [-5.27852488e+00, -9.87475681e+00, -3.23101878e+00,\n", + " -3.27192068e+00, 2.99915814e+00, -4.19678402e+00,\n", + " -6.34950256e+00, -4.51865005e+00, 7.18662143e-01,\n", + " 4.91613436e+00],\n", + " [ 5.31619835e+00, -4.94643354e+00, -7.60741353e-01,\n", + " -3.37821364e+00, -2.58448744e+00, -1.16258490e+00,\n", + " -2.44758511e+00, -2.42502451e+00, -2.97429585e+00,\n", + " -8.71329665e-01],\n", + " [-6.26879740e+00, 5.35215139e+00, -1.39423239e+00,\n", + " 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+ ] + } + ], + "source": [ + "prediction.data.numpy()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "inp = gradio.inputs.Sketchpad(flatten=True, scale=1/255, dtype='float32')\n", + "io = gradio.Interface(inputs=inp, outputs=\"label\", model_type=\"pytorch\", model=model)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No validation samples for this interface... skipping validation.\n", + "NOTE: Gradio is in beta stage, please report all bugs to: a12d@stanford.edu\n", + "Model is running locally at: http://localhost:7874/interface.html\n", + "To create a public link, set `share=True` in the argument to `launch()`\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(.HTTPServer at 0x1450966be48>,\n", + " 'http://localhost:7874/',\n", + " None)" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "float32\n", + "torch.float32\n" + ] + } + ], + "source": [ + "io.launch()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model" + ] + } + ], + "metadata": { + "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": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Test Sklearn.ipynb b/Test Sklearn.ipynb new file mode 100644 index 0000000000..dda79112bf --- /dev/null +++ b/Test Sklearn.ipynb @@ -0,0 +1,211 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "from sklearn import datasets, svm\n", + "import gradio\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# The digits dataset\n", + "digits = datasets.load_digits()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma=0.001, kernel='rbf',\n", + " max_iter=-1, probability=False, random_state=None, shrinking=True,\n", + " tol=0.001, verbose=False)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# To apply a classifier on this data, we need to flatten the image, to\n", + "# turn the data in a (samples, feature) matrix:\n", + "n_samples = len(digits.images)\n", + "data = digits.images.reshape((n_samples, -1))\n", + "\n", + "# Create a classifier: a support vector classifier\n", + "classifier = svm.SVC(gamma=0.001)\n", + "\n", + "# We learn the digits on the first half of the digits\n", + "classifier.fit(data[:n_samples // 2], digits.target[:n_samples // 2])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "16.0" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.max()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "images_and_labels = list(zip(digits.images, digits.target))\n", + "for index, (image, label) in enumerate(images_and_labels[:4]):\n", + " plt.subplot(2, 4, index + 1)\n", + " plt.axis('off')\n", + " plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')\n", + " plt.title('Training: %i' % label)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "classifier.predict()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "expected = digits.target[n_samples // 2:]\n", + "predicted = classifier.predict(data[n_samples // 2:])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "inp = gradio.inputs.Sketchpad(shape=(8, 8), flatten=True, scale=16/255, invert_colors=False)\n", + "io = gradio.Interface(inputs=inp, outputs=\"label\", model_type=\"sklearn\", model=classifier)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No validation samples for this interface... skipping validation.\n", + "NOTE: Gradio is in beta stage, please report all bugs to: a12d@stanford.edu\n", + "Model is running locally at: http://localhost:7865/interface.html\n", + "To create a public link, set `share=True` in the argument to `launch()`\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(.HTTPServer at 0x2a051e271d0>,\n", + " 'http://localhost:7865/',\n", + " None)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "io.launch()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.6 (tensorflow)", + "language": "python", + "name": "tensorflow" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Test Tensorflow.ipynb b/Test Tensorflow.ipynb new file mode 100644 index 0000000000..84cd66af4d --- /dev/null +++ b/Test Tensorflow.ipynb @@ -0,0 +1,196 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import tensorflow as tf\n", + "import gradio" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "n_classes = 10\n", + "(x_train, y_train),(x_test, y_test) = tf.keras.datasets.mnist.load_data()\n", + "x_train, x_test = x_train.reshape(-1, 784) / 255.0, x_test.reshape(-1, 784) / 255.0\n", + "y_train = tf.keras.utils.to_categorical(y_train, n_classes).astype(float)\n", + "y_test = tf.keras.utils.to_categorical(y_test, n_classes).astype(float)\n", + "\n", + "learning_rate = 0.5\n", + "epochs = 5\n", + "batch_size = 100" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "x = tf.placeholder(tf.float32, [None, 784], name=\"x\")\n", + "y = tf.placeholder(tf.float32, [None, 10], name=\"y\")\n", + "\n", + "W1 = tf.Variable(tf.random_normal([784, 300], stddev=0.03), name='W1')\n", + "b1 = tf.Variable(tf.random_normal([300]), name='b1')\n", + "W2 = tf.Variable(tf.random_normal([300, 10], stddev=0.03), name='W2')\n", + "hidden_out = tf.add(tf.matmul(x, W1), b1)\n", + "hidden_out = tf.nn.relu(hidden_out)\n", + "y_ = tf.matmul(hidden_out, W2)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "probs = tf.nn.softmax(y_)\n", + "cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=y_, labels=y))\n", + "optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cross_entropy)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "init_op = tf.global_variables_initializer()\n", + "correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))\n", + "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 1 cost = 0.317\n", + "Epoch: 2 cost = 0.123\n", + "Epoch: 3 cost = 0.086\n", + "Epoch: 4 cost = 0.066\n", + "Epoch: 5 cost = 0.052\n" + ] + } + ], + "source": [ + "sess = tf.Session()\n", + "sess.run(init_op)\n", + "total_batch = int(len(y_train) / batch_size)\n", + "for epoch in range(epochs):\n", + " avg_cost = 0\n", + " for start, end in zip(range(0, len(y_train), batch_size), range(batch_size, len(y_train)+1, batch_size)): \n", + " batch_x = x_train[start: end]\n", + " batch_y = y_train[start: end]\n", + " _, c = sess.run([optimizer, cross_entropy], feed_dict={x: batch_x, y: batch_y})\n", + " avg_cost += c / total_batch" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def predict(inp):\n", + " return sess.run(probs, feed_dict={x:inp})" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "inp = gradio.inputs.Sketchpad(flatten=True)\n", + "io = gradio.Interface(inputs=inp, outputs=\"label\", model_type=\"pyfunc\", model=predict)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No validation samples for this interface... skipping validation.\n", + "NOTE: Gradio is in beta stage, please report all bugs to: a12d@stanford.edu\n", + "Model is running locally at: http://localhost:7868/interface.html\n", + "To create a public link, set `share=True` in the argument to `launch()`\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(.HTTPServer at 0x2a1266b6b38>,\n", + " 'http://localhost:7868/',\n", + " None)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "io.launch()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.6 (tensorflow)", + "language": "python", + "name": "tensorflow" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/gradio/static/js/jquery-3.2.1.slim.min.js b/gradio/static/js/jquery-3.2.1.slim.min.js new file mode 100644 index 0000000000..105d00e61d --- /dev/null +++ b/gradio/static/js/jquery-3.2.1.slim.min.js @@ -0,0 +1,4 @@ +/*! jQuery v3.2.1 -ajax,-ajax/jsonp,-ajax/load,-ajax/parseXML,-ajax/script,-ajax/var/location,-ajax/var/nonce,-ajax/var/rquery,-ajax/xhr,-manipulation/_evalUrl,-event/ajax,-effects,-effects/Tween,-effects/animatedSelector | (c) JS Foundation and other contributors | jquery.org/license */ +!function(a,b){"use strict";"object"==typeof module&&"object"==typeof module.exports?module.exports=a.document?b(a,!0):function(a){if(!a.document)throw new Error("jQuery requires a window with a document");return b(a)}:b(a)}("undefined"!=typeof window?window:this,function(a,b){"use strict";var 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