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Unverified Commit abe47ae5 authored by zan's avatar zan Committed by GitHub
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Add graphs of percents for the prediction array

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samples/core/tutorials/keras/basic_classification.ipynb
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...@@ -848,46 +848,129 @@ ...@@ -848,46 +848,129 @@
}, },
{ {
"metadata": { "metadata": {
"id": "kgdvGD52CaXR", "id": "ygh2yYC972ne",
"colab_type": "text" "colab_type": "text"
}, },
"cell_type": "markdown", "cell_type": "markdown",
"source": [ "source": [
"Let's plot several images with their predictions. Correct prediction labels are green and incorrect prediction labels are red." "We can graph this to look at the full set of 10 channels"
] ]
}, },
{ {
"metadata": { "metadata": {
"id": "YGBDAiziCaXR", "id": "DvYmmrpIy6Y1",
"colab_type": "code", "colab_type": "code",
"colab": { "colab": {}
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
}, },
"cell_type": "code", "cell_type": "code",
"source": [ "source": [
"# Plot the first 25 test images, their predicted label, and the true label\n", "def plot_image(predictions_array, true_label, img):\n",
"# Color correct predictions in green, incorrect predictions in red\n",
"plt.figure(figsize=(10,10))\n",
"for i in range(25):\n",
" plt.subplot(5,5,i+1)\n",
" plt.xticks([])\n", " plt.xticks([])\n",
" plt.yticks([])\n", " plt.yticks([])\n",
" plt.grid('off')\n", " plt.grid('off')\n",
" plt.imshow(test_images[i], cmap=plt.cm.binary)\n", " plt.imshow(img, cmap=plt.cm.binary)\n",
" predicted_label = np.argmax(predictions[i])\n", "\n",
" true_label = test_labels[i]\n", " predicted_label = np.argmax(predictions_array)\n",
" if predicted_label == true_label:\n", " if predicted_label == true_label:\n",
" color = 'green'\n", " color = 'blue'\n",
" else:\n", " else:\n",
" color = 'red'\n", " color = 'red'\n",
" plt.xlabel(\"{} ({})\".format(class_names[predicted_label], \n", " plt.xlabel(\"{} {} ({})\".format(class_names[predicted_label],\n",
" str(round(predictions_array[np.argmax(predictions_array)] * 100)) + \"%\",\n",
" class_names[true_label]),\n", " class_names[true_label]),\n",
" color=color)\n", " color=color)\n",
" " "\n",
"def plot_value_array(predictions_array, true_label):\n",
" plt.grid('off')\n",
" thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n",
" plt.ylim([0, 1]) \n",
" predicted_label = np.argmax(predictions_array)\n",
" \n",
" thisplot[predicted_label].set_color('red')\n",
" thisplot[true_label].set_color('blue')\n",
"\n",
" \n",
"# define plot to look at the image, predicted label, actual label, predicted percent for top prediction, and graph of all prediction values\n",
"def plot_fig_and_predarray(iter):\n",
" plt.figure(figsize=(6,3))\n",
" \n",
" # plot the image first\n",
" plt.subplot(1,2,1)\n",
" plot_image(predictions[iter], test_labels[iter], test_images[int(iter)])\n",
" \n",
" # then the graph of 10 values\n",
" plt.subplot(1,2,2)\n",
" plot_value_array(predictions[iter], test_labels[iter])"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "d4Ov9OFDMmOD",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"Let's look at the 0th image, predictions, and prediction array. "
]
},
{
"metadata": {
"id": "HV5jw-5HwSmO",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"plot_fig_and_predarray(0)"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "Ko-uzOufSCSe",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"plot_fig_and_predarray(12)"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "kgdvGD52CaXR",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"Let's plot several images with their predictions. Correct prediction labels are blue and incorrect prediction labels are red. The number gives the percent (out of 100) for the predicted label. Note that it can be wrong even when very confident. "
]
},
{
"metadata": {
"id": "hQlnbqaw2Qu_",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"# Plot the first X test images, their predicted label, and the true label\n",
"# Color correct predictions in blue, incorrect predictions in red\n",
"num_images = int(50)\n",
"plt.figure(figsize=(24,20))\n",
"for i in range(num_images):\n",
" plt.subplot(10,num_images / 5,2*i+1)\n",
" plot_image(predictions[i], test_labels[i], test_images[i])\n",
" \n",
" plt.subplot(10,num_images / 5,2*i+2)\n",
" plt.xticks([])\n",
" plt.yticks([])\n",
" plot_value_array(predictions[i], test_labels[i])"
], ],
"execution_count": 0, "execution_count": 0,
"outputs": [] "outputs": []
...@@ -906,12 +989,7 @@ ...@@ -906,12 +989,7 @@
"metadata": { "metadata": {
"id": "yRJ7JU7JCaXT", "id": "yRJ7JU7JCaXT",
"colab_type": "code", "colab_type": "code",
"colab": { "colab": {}
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
}, },
"cell_type": "code", "cell_type": "code",
"source": [ "source": [
...@@ -937,12 +1015,7 @@ ...@@ -937,12 +1015,7 @@
"metadata": { "metadata": {
"id": "lDFh5yF_CaXW", "id": "lDFh5yF_CaXW",
"colab_type": "code", "colab_type": "code",
"colab": { "colab": {}
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
}, },
"cell_type": "code", "cell_type": "code",
"source": [ "source": [
...@@ -968,18 +1041,13 @@ ...@@ -968,18 +1041,13 @@
"metadata": { "metadata": {
"id": "o_rzNSdrCaXY", "id": "o_rzNSdrCaXY",
"colab_type": "code", "colab_type": "code",
"colab": { "colab": {}
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
}, },
"cell_type": "code", "cell_type": "code",
"source": [ "source": [
"predictions = model.predict(img)\n", "predictions_single = model.predict(img)\n",
"\n", "\n",
"print(predictions)" "print(predictions_single)"
], ],
"execution_count": 0, "execution_count": 0,
"outputs": [] "outputs": []
...@@ -998,18 +1066,11 @@ ...@@ -998,18 +1066,11 @@
"metadata": { "metadata": {
"id": "2tRmdq_8CaXb", "id": "2tRmdq_8CaXb",
"colab_type": "code", "colab_type": "code",
"colab": { "colab": {}
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
}, },
"cell_type": "code", "cell_type": "code",
"source": [ "source": [
"prediction = predictions[0]\n", "np.argmax(predictions_single[0])"
"\n",
"np.argmax(prediction)"
], ],
"execution_count": 0, "execution_count": 0,
"outputs": [] "outputs": []
......
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