Run "git add --renormalize ." and clear all outputs

deephyper · Jan 20, 2022 · 82eab27 · 82eab27
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diff --git a/01_machineLearning/part-1_introduction-to-sklearn/0_linear-models.ipynb b/01_machineLearning/part-1_introduction-to-sklearn/0_linear-models.ipynb
diff --git a/01_machineLearning/part-1_introduction-to-sklearn/1_kernel-methods.ipynb b/01_machineLearning/part-1_introduction-to-sklearn/1_kernel-methods.ipynb
diff --git a/01_machineLearning/part-2_ml-with-materials-data/1_ml-with-fingerprints.ipynb b/01_machineLearning/part-2_ml-with-materials-data/1_ml-with-fingerprints.ipynb
diff --git a/02_deepLearning/01_Intro.ipynb b/02_deepLearning/01_Intro.ipynb
diff --git a/02_deepLearning/02_Mnist.ipynb b/02_deepLearning/02_Mnist.ipynb
diff --git a/02_deepLearning/03_CIFAR-10.ipynb b/02_deepLearning/03_CIFAR-10.ipynb
diff --git a/03_dataPipelines/00_tensorflowDatasetAPI/inspect_pipeline.ipynb b/03_dataPipelines/00_tensorflowDatasetAPI/inspect_pipeline.ipynb
diff --git a/04_images_time_series/00_images/Image_Segmentation.ipynb b/04_images_time_series/00_images/Image_Segmentation.ipynb
diff --git a/04_images_time_series/01_time_series/CAE_LSTM.ipynb b/04_images_time_series/01_time_series/CAE_LSTM.ipynb
@@ -93,9 +93,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# timestep, y, x, variable (channel)\n",
@@ -149,9 +147,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Visualize multiple time instances\n",
@@ -300,9 +296,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "time = 100\n",
@@ -359,9 +353,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "encoded = K.eval(encoder(swe_train_data[:,:,:,:].astype('float32')))\n",
@@ -437,9 +429,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Model architecture\n",
@@ -543,9 +533,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "for i in range(4):\n",
@@ -631,9 +619,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "x = np.linspace(-1/2, 1/2, 64)  # Array with x-points\n",