rerun of notebook
"import matplotlib.pyplot as plt\n",
"from matplotlib import cm\n",
"import matplotlib\n",
- "matplotlib.style.use('seaborn')\n",
"%matplotlib inline\n",
+ "matplotlib.style.use('seaborn')\n",
"labels_per_trial_per_crop = model.predict_classes(test_set.X, individual_crops=True)\n",
"accs_per_crop = [l == y for l,y in zip(labels_per_trial_per_crop, test_set.y)]\n",
"accs_per_crop = np.mean(accs_per_crop, axis=0)\n",
"plt.figure(figsize=(8,3))\n",
"plt.plot(accs_per_crop * 100)\n",
- "plt.xlabel('Timestep in trial')\n",
- "plt.ylabel('Accuracies at timestep [%]')"
+ "plt.title(\"Accuracies per timestep\", fontsize=16)\n",
+ "plt.xlabel('Timestep in trial', fontsize=14)\n",
+ "plt.ylabel('Accuracy [%]', fontsize=14)"
]
},
{
"i_trial = 0\n",
"# log-softmax outputs need to be exponentiated to get probabilities\n",
"cropped_probs = np.exp(cropped_outs[i_trial])\n",
+ "plt.figure(figsize=(8,3))\n",
"plt.plot(cropped_probs.T)\n",
"plt.title(\"Network probabilities for trial {:d} of class {:d}\".format(\n",
- " i_trial, test_set.y[i_trial]))\n",
- "plt.legend((\"Class 0\", \"Class 1\"))\n",
- "plt.xlabel(\"Timestep within trial\")\n",
- "plt.ylabel(\"Probabilities\")"
+ " i_trial, test_set.y[i_trial]), fontsize=16)\n",
+ "plt.legend((\"Class 0\", \"Class 1\"), fontsize=12)\n",
+ "plt.xlabel(\"Timestep within trial\", fontsize=14)\n",
+ "plt.ylabel(\"Probabilities\", fontsize=14)"
]
},
{
