diff --git "a/EDA.ipynb" "b/EDA.ipynb"
--- "a/EDA.ipynb"
+++ "b/EDA.ipynb"
@@ -16,9 +16,14 @@
" - [π§ Image exploration](#toc3_3_) \n",
"- [Data preprocessing](#toc4_) \n",
"- [π¦π¦ CHECKPOINT π¦π¦](#toc5_) \n",
- "- [Model Training](#toc6_) \n",
- "- [π§ fin Code JL](#toc7_) \n",
- " - [Random Baseline](#toc7_1_) \n",
+ "- [π§ OVERSAMPLING](#toc6_) \n",
+ "- [Model Training](#toc7_) \n",
+ " - [Load configuration](#toc7_1_) \n",
+ "- [π§ MODEL CHOICE](#toc8_) \n",
+ " - [Prepare data](#toc8_1_) \n",
+ " - [Prepare model](#toc8_2_) \n",
+ " - [Training](#toc8_3_) \n",
+ " - [Random Baseline](#toc8_4_) \n",
"\n",
" 1\u001b[0m \u001b[43mstop\u001b[49m\n",
+ "Cell \u001b[0;32mIn[16], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mstop\u001b[49m\n",
"\u001b[0;31mNameError\u001b[0m: name 'stop' is not defined"
]
}
@@ -2239,21 +2883,21 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# [Model Training](#toc0_)"
+ "# [Model Training](#toc0_)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Load configuration"
+ "## [Load configuration](#toc0_)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "# π§ MODEL CHOICE\n",
+ "# [π§ MODEL CHOICE](#toc0_)\n",
"\n",
"- https://paperswithcode.com/sota/image-classification-on-imagenet\n",
"- https://keras.io/api/applications/efficientnet_v2/#efficientnetv2m-function"
@@ -2272,7 +2916,7 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 183,
"metadata": {},
"outputs": [],
"source": [
@@ -2301,7 +2945,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Prepare data"
+ "## [Prepare data](#toc0_)"
]
},
{
@@ -2313,7 +2957,7 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 184,
"metadata": {},
"outputs": [
{
@@ -2376,32 +3020,22 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Prepare model"
+ "## [Prepare model](#toc0_)"
]
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 187,
"metadata": {},
"outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/julmat/Documents/hugging_face/frugal_cviz/.venv/lib/python3.12/site-packages/keras/src/layers/layer.py:393: UserWarning:\n",
- "\n",
- "`build()` was called on layer 'conditional_augmentation_3', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method.\n",
- "\n"
- ]
- },
{
"data": {
"text/html": [
- "Model: \"functional_3\"\n",
+ "Model: \"functional_6\"\n",
"\n"
],
"text/plain": [
- "\u001b[1mModel: \"functional_3\"\u001b[0m\n"
+ "\u001b[1mModel: \"functional_6\"\u001b[0m\n"
]
},
"metadata": {},
@@ -2413,16 +3047,13 @@
"βββββββββββββββββββββββββββββββββββ³βββββββββββββββββββββββββ³ββββββββββββββββ\n",
"β Layer (type) β Output Shape β Param # β\n",
"β‘βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ©\n",
- "β input_layer_7 (InputLayer) β (None, 224, 224, 3) β 0 β\n",
- "βββββββββββββββββββββββββββββββββββΌβββββββββββββββββββββββββΌββββββββββββββββ€\n",
- "β conditional_augmentation_3 β (None, 224, 224, 3) β 0 β\n",
- "β (ConditionalAugmentation) β β β\n",
+ "β input_layer_13 (InputLayer) β (None, 224, 224, 3) β 0 β\n",
"βββββββββββββββββββββββββββββββββββΌβββββββββββββββββββββββββΌββββββββββββββββ€\n",
"β efficientnetb0 (Functional) β (None, 7, 7, 1280) β 4,049,571 β\n",
"βββββββββββββββββββββββββββββββββββΌβββββββββββββββββββββββββΌββββββββββββββββ€\n",
- "β flatten_3 (Flatten) β (None, 62720) β 0 β\n",
+ "β flatten_6 (Flatten) β (None, 62720) β 0 β\n",
"βββββββββββββββββββββββββββββββββββΌβββββββββββββββββββββββββΌββββββββββββββββ€\n",
- "β dense_3 (Dense) β (None, 1) β 62,721 β\n",
+ "β dense_6 (Dense) β (None, 1) β 62,721 β\n",
"βββββββββββββββββββββββββββββββββββ΄βββββββββββββββββββββββββ΄ββββββββββββββββ\n",
"\n"
],
@@ -2430,16 +3061,13 @@
"βββββββββββββββββββββββββββββββββββ³βββββββββββββββββββββββββ³ββββββββββββββββ\n",
"β\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0mβ\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0mβ\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0mβ\n",
"β‘βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ©\n",
- "β input_layer_7 (\u001b[38;5;33mInputLayer\u001b[0m) β (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m3\u001b[0m) β \u001b[38;5;34m0\u001b[0m β\n",
- "βββββββββββββββββββββββββββββββββββΌβββββββββββββββββββββββββΌββββββββββββββββ€\n",
- "β conditional_augmentation_3 β (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m3\u001b[0m) β \u001b[38;5;34m0\u001b[0m β\n",
- "β (\u001b[38;5;33mConditionalAugmentation\u001b[0m) β β β\n",
+ "β input_layer_13 (\u001b[38;5;33mInputLayer\u001b[0m) β (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m3\u001b[0m) β \u001b[38;5;34m0\u001b[0m β\n",
"βββββββββββββββββββββββββββββββββββΌβββββββββββββββββββββββββΌββββββββββββββββ€\n",
"β efficientnetb0 (\u001b[38;5;33mFunctional\u001b[0m) β (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m1280\u001b[0m) β \u001b[38;5;34m4,049,571\u001b[0m β\n",
"βββββββββββββββββββββββββββββββββββΌβββββββββββββββββββββββββΌββββββββββββββββ€\n",
- "β flatten_3 (\u001b[38;5;33mFlatten\u001b[0m) β (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m62720\u001b[0m) β \u001b[38;5;34m0\u001b[0m β\n",
+ "β flatten_6 (\u001b[38;5;33mFlatten\u001b[0m) β (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m62720\u001b[0m) β \u001b[38;5;34m0\u001b[0m β\n",
"βββββββββββββββββββββββββββββββββββΌβββββββββββββββββββββββββΌββββββββββββββββ€\n",
- "β dense_3 (\u001b[38;5;33mDense\u001b[0m) β (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) β \u001b[38;5;34m62,721\u001b[0m β\n",
+ "β dense_6 (\u001b[38;5;33mDense\u001b[0m) β (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) β \u001b[38;5;34m62,721\u001b[0m β\n",
"βββββββββββββββββββββββββββββββββββ΄βββββββββββββββββββββββββ΄ββββββββββββββββ\n"
]
},
@@ -2496,8 +3124,9 @@
"# Create explicit input layer\n",
"inputs = tf.keras.Input(shape=(input_size[0], input_size[1], 3))\n",
"# add data augmentation\n",
- "augmented = ConditionalAugmentation(rate=0.4)(inputs)\n",
- "x = model(augmented)\n",
+ "# augmented = ConditionalAugmentation(rate=0.4)(inputs)\n",
+ "# x = model(augmented)\n",
+ "x = model(inputs)\n",
"\n",
"# Flatten output\n",
"x = Flatten()(x)\n",
@@ -2516,7 +3145,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Training"
+ "## [Training](#toc0_)"
]
},
{
@@ -2674,7 +3303,7 @@
},
{
"cell_type": "code",
- "execution_count": 33,
+ "execution_count": null,
"metadata": {},
"outputs": [
{
@@ -2737,7 +3366,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## [Random Baseline](#toc0_)"
+ "## [Random Baseline](#toc0_)"
]
},
{