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March 29, 2018 13:39
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": { | |
| "colab": { | |
| "autoexec": { | |
| "startup": false, | |
| "wait_interval": 0 | |
| } | |
| }, | |
| "colab_type": "code", | |
| "id": "NHu1BI5G6Bzn" | |
| }, | |
| "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" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# -*- coding: utf-8 -*-\n", | |
| "\"\"\"\n", | |
| "Task : locate any point from of non-distinguishable target points.\n", | |
| "The MAE+\"negative gaussian\" loss works better than MSE or MAE. \n", | |
| "\"\"\"\n", | |
| "\n", | |
| "import numpy as np\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "\n", | |
| "size = 16 # image size\n", | |
| "\n", | |
| "# prepare data\n", | |
| "def generate_data(num_samples=30000,num_points=2,seed=0):\n", | |
| " np.random.seed(seed)\n", | |
| " imgs = np.zeros((num_samples,size,size,3))\n", | |
| " coords = np.random.random((num_points,num_samples,2))\n", | |
| " points = (coords*size).astype(np.int32)\n", | |
| " for p in points:\n", | |
| " imgs[range(num_samples),p[:,0],p[:,1]] = 1\n", | |
| " return imgs,coords\n", | |
| " \n", | |
| "imgs,coords = generate_data()\n", | |
| "\n", | |
| "\n", | |
| "# model\n", | |
| "import keras\n", | |
| "from keras.models import Model\n", | |
| "from keras.layers import Input,Flatten,Dense\n", | |
| "from keras.layers.convolutional import Conv2D\n", | |
| "from keras import backend as K\n", | |
| "\n", | |
| "num_epochs = 5\n", | |
| "batch_size = 16\n", | |
| "\n", | |
| "x = Input(shape=(size,size,1))\n", | |
| "h = Conv2D(2,(5,5))(x)\n", | |
| "h = Flatten()(h)\n", | |
| "h = Dense(16,activation=\"relu\")(h)\n", | |
| "y = Dense(2)(h)\n", | |
| "model = Model(inputs=[x],outputs=[y])\n", | |
| "\n", | |
| "# test\n", | |
| "def test_model(model):\n", | |
| " n = 100\n", | |
| " disp = 10\n", | |
| " imgs_test, _ = generate_data(n,seed=1)\n", | |
| " correct = 0\n", | |
| " for i in range(n):\n", | |
| " x = np.copy(imgs_test[i:i+1])\n", | |
| " v = model.predict(x[:,:,:,0:1])[0]\n", | |
| " v = np.clip(v,0,0.99)\n", | |
| " v = (v*size).astype(np.int32)\n", | |
| "\n", | |
| " if x[:,v[0],v[1],0] == 1:\n", | |
| " correct += 1\n", | |
| " x[:,v[0],v[1]] = (0,1,0)\n", | |
| " else:\n", | |
| " x[:,v[0],v[1]] = (0,1,1)\n", | |
| " \n", | |
| " if i<disp:\n", | |
| " plt.imshow(x[0])\n", | |
| " plt.show()\n", | |
| " \n", | |
| " print(\"hit rate: \",correct/n)\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": { | |
| "colab": { | |
| "autoexec": { | |
| "startup": false, | |
| "wait_interval": 0 | |
| }, | |
| "base_uri": "https://localhost:8080/", | |
| "height": 10617, | |
| "output_extras": [ | |
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| }, | |
| "colab_type": "code", | |
| "executionInfo": { | |
| "elapsed": 54291, | |
| "status": "ok", | |
| "timestamp": 1522328991786, | |
| "user": { | |
| "displayName": "Satoshi Tanaka", | |
| "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128", | |
| "userId": "109284846876711930713" | |
| }, | |
| "user_tz": -540 | |
| }, | |
| "id": "31rBEBo937wW", | |
| "outputId": "e4c712b1-387d-4dda-ff85-370bdf0b0acb" | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Epoch 1/5\n", | |
| "30000/30000 [==============================] - 5s 160us/step - loss: 0.0498 - mean_absolute_error: 0.1801\n", | |
| "Epoch 2/5\n", | |
| "30000/30000 [==============================] - 4s 133us/step - loss: 0.0442 - mean_absolute_error: 0.1712\n", | |
| "Epoch 3/5\n", | |
| "30000/30000 [==============================] - 4s 133us/step - loss: 0.0437 - mean_absolute_error: 0.1704\n", | |
| "Epoch 4/5\n", | |
| "30000/30000 [==============================] - 4s 133us/step - loss: 0.0434 - mean_absolute_error: 0.1700\n", | |
| "Epoch 5/5\n", | |
| "30000/30000 [==============================] - 4s 134us/step - loss: 0.0433 - mean_absolute_error: 0.1697\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
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| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f83f400e1d0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
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| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f83f066b240>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
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| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f83f0691588>" | |
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| "output_type": "display_data" | |
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| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f83f064d518>" | |
| ] | |
| }, | |
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| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f83f0695320>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
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| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f83f01d9160>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
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| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f83f0735fd0>" | |
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| "metadata": {}, | |
| "output_type": "display_data" | |
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| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f83f062acf8>" | |
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| "metadata": {}, | |
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| }, | |
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| "<matplotlib.figure.Figure at 0x7f83f077f160>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "hit rate: 0.0\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "model.compile(loss=\"mean_squared_error\", optimizer=keras.optimizers.Nadam(), metrics=[\"mean_absolute_error\"])\n", | |
| "model.fit(imgs[:,:,:,0:1],coords[0],batch_size=batch_size,epochs=num_epochs,verbose=1)\n", | |
| "\n", | |
| "test_model(model) # hit rate: 0.00" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": { | |
| "colab": { | |
| "autoexec": { | |
| "startup": false, | |
| "wait_interval": 0 | |
| }, | |
| "base_uri": "https://localhost:8080/", | |
| "height": 10617, | |
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| }, | |
| "colab_type": "code", | |
| "executionInfo": { | |
| "elapsed": 56389, | |
| "status": "ok", | |
| "timestamp": 1522329102892, | |
| "user": { | |
| "displayName": "Satoshi Tanaka", | |
| "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128", | |
| "userId": "109284846876711930713" | |
| }, | |
| "user_tz": -540 | |
| }, | |
| "id": "SROGo6Hc4DTj", | |
| "outputId": "e83ce3c9-385d-4fe8-da97-57f8d26a12fe" | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Epoch 1/5\n", | |
| "30000/30000 [==============================] - 4s 149us/step - loss: 0.1696 - mean_absolute_error: 0.1696\n", | |
| "Epoch 2/5\n", | |
| "30000/30000 [==============================] - 4s 135us/step - loss: 0.1682 - mean_absolute_error: 0.1682\n", | |
| "Epoch 3/5\n", | |
| "30000/30000 [==============================] - 4s 134us/step - loss: 0.1674 - mean_absolute_error: 0.1674\n", | |
| "Epoch 4/5\n", | |
| "30000/30000 [==============================] - 4s 134us/step - loss: 0.1665 - mean_absolute_error: 0.1665\n", | |
| "Epoch 5/5\n", | |
| "30000/30000 [==============================] - 4s 133us/step - loss: 0.1660 - mean_absolute_error: 0.1660\n" | |
| ] | |
| }, | |
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| "data": { | |
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| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f83f436e358>" | |
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| "output_type": "display_data" | |
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| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f83f3fafc88>" | |
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| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f83e9697eb8>" | |
| ] | |
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| "metadata": {}, | |
| "output_type": "display_data" | |
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| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f83e9624d68>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
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| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f83e964c470>" | |
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| "output_type": "display_data" | |
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| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f83e9635780>" | |
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| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
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| "<matplotlib.figure.Figure at 0x7f83e96eeac8>" | |
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| "output_type": "display_data" | |
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| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f83f0766c18>" | |
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| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f83f06ef7b8>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "hit rate: 0.01\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "model.compile(loss=\"mean_absolute_error\", optimizer=keras.optimizers.Nadam(), metrics=[\"mean_absolute_error\"])\n", | |
| "model.fit(imgs[:,:,:,0:1],coords[0],batch_size=batch_size,epochs=num_epochs,verbose=1)\n", | |
| "\n", | |
| "test_model(model) # hit rate: 0.01" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": { | |
| "colab": { | |
| "autoexec": { | |
| "startup": false, | |
| "wait_interval": 0 | |
| }, | |
| "base_uri": "https://localhost:8080/", | |
| "height": 10617, | |
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| }, | |
| "colab_type": "code", | |
| "executionInfo": { | |
| "elapsed": 56541, | |
| "status": "ok", | |
| "timestamp": 1522329159446, | |
| "user": { | |
| "displayName": "Satoshi Tanaka", | |
| "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128", | |
| "userId": "109284846876711930713" | |
| }, | |
| "user_tz": -540 | |
| }, | |
| "id": "znCjwrpG4E3N", | |
| "outputId": "fefe2792-37e5-49c8-c8ed-990a7296fe5b" | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Epoch 1/5\n", | |
| "30000/30000 [==============================] - 5s 166us/step - loss: 0.8947 - mean_absolute_error: 0.1727\n", | |
| "Epoch 2/5\n", | |
| "30000/30000 [==============================] - 5s 151us/step - loss: 0.8280 - mean_absolute_error: 0.1742\n", | |
| "Epoch 3/5\n", | |
| "30000/30000 [==============================] - 5s 153us/step - loss: 0.8056 - mean_absolute_error: 0.1742\n", | |
| "Epoch 4/5\n", | |
| "30000/30000 [==============================] - 5s 152us/step - loss: 0.7957 - mean_absolute_error: 0.1739\n", | |
| "Epoch 5/5\n", | |
| "30000/30000 [==============================] - 5s 151us/step - loss: 0.7893 - mean_absolute_error: 0.1739\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
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| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f83f0144c50>" | |
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| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
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| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f83f062a0b8>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
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| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f83e96c7ef0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
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| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f83f014ce80>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
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| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f83bc381a90>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
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| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f83e96afdd8>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
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| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f83e964e470>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
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| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f83e9633128>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "image/png": "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\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f83bc2a44a8>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "hit rate: 0.6\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "def custom_loss(y_true,y_pred):\n", | |
| " # MAE + negative gaussian\n", | |
| " sigma = 0.1\n", | |
| " return K.mean(K.abs(y_pred - y_true), axis=-1) + 1-K.exp( - ( (y_pred[:,0]-y_true[:,0])**2+(y_pred[:,1]-y_true[:,1])**2 )/sigma**2 )\n", | |
| "\n", | |
| "model.compile(loss=custom_loss, optimizer=keras.optimizers.Nadam(), metrics=[\"mean_absolute_error\"])\n", | |
| "model.fit(imgs[:,:,:,0:1],coords[0],batch_size=batch_size,epochs=num_epochs,verbose=1)\n", | |
| "\n", | |
| "test_model(model) # hit rate: 0.60" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [] | |
| } | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "collapsed_sections": [], | |
| "default_view": {}, | |
| "name": "double_target", | |
| "provenance": [], | |
| "toc_visible": true, | |
| "version": "0.3.2", | |
| "views": {} | |
| }, | |
| "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": "3.5.2+" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 1 | |
| } |
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