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Unverified Commit 51cf5304 authored by zan's avatar zan Committed by GitHub
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Merge pull request #1 from zanarmstrong/zanarmstrong-graphs 

Add graphs of percents for the prediction array
parents c2f05afc abe47ae5
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Showing with 116 additions and 55 deletions
samples/core/tutorials/keras/basic_classification.ipynb
View file @ 51cf5304
...@@ -845,6 +845,101 @@ ...@@ -845,6 +845,101 @@
], ],
"execution_count": 0, "execution_count": 0,
"outputs": [] "outputs": []
},
{
"metadata": {
"id": "ygh2yYC972ne",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"We can graph this to look at the full set of 10 channels"
]
},
{
"metadata": {
"id": "DvYmmrpIy6Y1",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"def plot_image(predictions_array, true_label, img):\n",
" plt.xticks([])\n",
" plt.yticks([])\n",
" plt.grid('off')\n",
" plt.imshow(img, cmap=plt.cm.binary)\n",
"\n",
" predicted_label = np.argmax(predictions_array)\n",
" if predicted_label == true_label:\n",
" color = 'blue'\n",
" else:\n",
" color = 'red'\n",
" plt.xlabel(\"{} {} ({})\".format(class_names[predicted_label],\n",
" str(round(predictions_array[np.argmax(predictions_array)] * 100)) + \"%\",\n",
" class_names[true_label]),\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": { "metadata": {
...@@ -853,41 +948,29 @@ ...@@ -853,41 +948,29 @@
}, },
"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." "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": { "metadata": {
"id": "YGBDAiziCaXR", "id": "hQlnbqaw2Qu_",
"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", "# Plot the first X test images, their predicted label, and the true label\n",
"# Color correct predictions in green, incorrect predictions in red\n", "# Color correct predictions in blue, incorrect predictions in red\n",
"plt.figure(figsize=(10,10))\n", "num_images = int(50)\n",
"for i in range(25):\n", "plt.figure(figsize=(24,20))\n",
" plt.subplot(5,5,i+1)\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.xticks([])\n",
" plt.yticks([])\n", " plt.yticks([])\n",
" plt.grid('off')\n", " plot_value_array(predictions[i], test_labels[i])"
" plt.imshow(test_images[i], cmap=plt.cm.binary)\n",
" predicted_label = np.argmax(predictions[i])\n",
" true_label = test_labels[i]\n",
" if predicted_label == true_label:\n",
" color = 'green'\n",
" else:\n",
" color = 'red'\n",
" plt.xlabel(\"{} ({})\".format(class_names[predicted_label], \n",
" class_names[true_label]),\n",
" color=color)\n",
" "
], ],
"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": []
...@@ -1025,4 +1086,4 @@ ...@@ -1025,4 +1086,4 @@
] ]
} }
] ]
} }
\ No newline at end of file
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