diff --git a/Animation - 1719728959093.json b/Animation - 1719728959093.json
new file mode 100644
index 0000000000000000000000000000000000000000..2263ab2a14bcaf756a8ab7d910bda20c7a3e1413
--- /dev/null
+++ b/Animation - 1719728959093.json	
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\ No newline at end of file
diff --git a/README.md b/README.md
index 0b39d6bbecf9dffa70058eba2e1eb47d38476faa..6a2e55ee83801882fe7d4128483972ad725307e0 100644
--- a/README.md
+++ b/README.md
@@ -1,13 +1 @@
----
-title: TraiNNe
-emoji: 🐢
-colorFrom: gray
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.40.1
-app_file: app.py
-pinned: false
-short_description: understanding hyper parameter tuning in NN
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# NNbasics
\ No newline at end of file
diff --git a/__pycache__/code.cpython-310.pyc b/__pycache__/code.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..701a3bb2bf9977b2c8ff5b16cb791c4f706f92db
Binary files /dev/null and b/__pycache__/code.cpython-310.pyc differ
diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..dbab949d3ba5073fa092e9509594ce0755d9194b
--- /dev/null
+++ b/app.py
@@ -0,0 +1,63 @@
+
+import streamlit as st
+import tensorflow as tf
+from tensorflow.keras.datasets import mnist
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, Flatten
+from tensorflow.keras.optimizers import Adam
+import matplotlib.pyplot as plt
+
+# Load MNIST data
+(x_train, y_train), (x_test, y_test) = mnist.load_data()
+x_train, x_test = x_train / 255.0, x_test / 255.0
+
+# Streamlit app
+st.title("MNIST Neural Network Training")
+
+# Sidebar for parameter selection
+st.sidebar.header("Model Parameters")
+learning_rate = st.sidebar.slider("Learning Rate", 0.0001, 0.01, 0.001)
+batch_size = st.sidebar.slider("Batch Size", 16, 128, 32)
+epochs = st.sidebar.slider("Epochs", 1, 20, 5)
+
+# Model building function
+def build_model(learning_rate):
+    model = Sequential([
+        Flatten(input_shape=(28, 28)),
+        Dense(128, activation='relu'),
+        Dense(10, activation='softmax')
+    ])
+    model.compile(optimizer=Adam(learning_rate=learning_rate),
+                  loss='sparse_categorical_crossentropy',
+                  metrics=['accuracy'])
+    return model
+
+# Train the model
+if st.sidebar.button("Train Model"):
+    model = build_model(learning_rate)
+    history = model.fit(x_train, y_train, epochs=epochs, batch_size=batch_size, validation_data=(x_test, y_test))
+
+    # Plot training & validation accuracy values
+    fig, ax = plt.subplots()
+    ax.plot(history.history['accuracy'])
+    ax.plot(history.history['val_accuracy'])
+    ax.set_title('Model accuracy')
+    ax.set_ylabel('Accuracy')
+    ax.set_xlabel('Epoch')
+    ax.legend(['Train', 'Test'], loc='upper left')
+    st.pyplot(fig)
+
+    # Plot training & validation loss values
+    fig, ax = plt.subplots()
+    ax.plot(history.history['loss'])
+    ax.plot(history.history['val_loss'])
+    ax.set_title('Model loss')
+    ax.set_ylabel('Loss')
+    ax.set_xlabel('Epoch')
+    ax.legend(['Train', 'Test'], loc='upper left')
+    st.pyplot(fig)
+
+    # Evaluate the model
+    loss, accuracy = model.evaluate(x_test, y_test, verbose=2)
+    st.write(f"Test Accuracy: {accuracy:.4f}")
+    st.write(f"Test Loss: {loss:.4f}")
diff --git a/f.ipynb b/f.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..58fc58e9e7b873180e61b2983d0fcb65d59843f2
--- /dev/null
+++ b/f.ipynb
@@ -0,0 +1,788 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Record 1:\n",
+      "Image:\n",
+      "[[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
+      "    0   0   0   0   0   0   0   0   0   0]\n",
+      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
+      "    0   0   0   0   0   0   0   0   0   0]\n",
+      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
+      "    0   0   0   0   0   0   0   0   0   0]\n",
+      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
+      "    0   0   0   0   0   0   0   0   0   0]\n",
+      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
+      "    0   0   0   0   0   0   0   0   0   0]\n",
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+      "Label: 9\n",
+      "\n",
+      "\n",
+      "Record 6:\n",
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+      "Label: 2\n",
+      "\n",
+      "\n",
+      "Record 7:\n",
+      "Image:\n",
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+      "Label: 1\n",
+      "\n",
+      "\n",
+      "Record 8:\n",
+      "Image:\n",
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+      "    0   0   0   0   0   0   0   0   0   0]]\n",
+      "Label: 3\n",
+      "\n",
+      "\n",
+      "Record 9:\n",
+      "Image:\n",
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+      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0 230 247  40   0\n",
+      "    0   0   0   0   0   0   0   0   0   0]\n",
+      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0 168 209  31   0\n",
+      "    0   0   0   0   0   0   0   0   0   0]\n",
+      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
+      "    0   0   0   0   0   0   0   0   0   0]\n",
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+      "    0   0   0   0   0   0   0   0   0   0]]\n",
+      "Label: 1\n",
+      "\n",
+      "\n",
+      "Record 10:\n",
+      "Image:\n",
+      "[[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
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+      " [  0   0   0   0   0   0 111  29   0   0   0   0  62 239 252  86  42  42\n",
+      "   14   0   0   0   0   0   0   0   0   0]\n",
+      " [  0   0   0   0   0   0   0   0   0   0   0  15 148 253 218   0   0   0\n",
+      "    0   0   0   0   0   0   0   0   0   0]\n",
+      " [  0   0   0   0   0   0   0   0   0   0   0 121 252 231  28   0   0   0\n",
+      "    0   0   0   0   0   0   0   0   0   0]\n",
+      " [  0   0   0   0   0   0   0   0   0   0  31 221 251 129   0   0   0   0\n",
+      "    0   0   0   0   0   0   0   0   0   0]\n",
+      " [  0   0   0   0   0   0   0   0   0   0 218 252 160   0   0   0   0   0\n",
+      "    0   0   0   0   0   0   0   0   0   0]\n",
+      " [  0   0   0   0   0   0   0   0   0   0 122 252  82   0   0   0   0   0\n",
+      "    0   0   0   0   0   0   0   0   0   0]\n",
+      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
+      "    0   0   0   0   0   0   0   0   0   0]\n",
+      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
+      "    0   0   0   0   0   0   0   0   0   0]\n",
+      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
+      "    0   0   0   0   0   0   0   0   0   0]\n",
+      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
+      "    0   0   0   0   0   0   0   0   0   0]]\n",
+      "Label: 4\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "from tensorflow.keras.datasets import mnist\n",
+    "\n",
+    "# Load MNIST data\n",
+    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
+    "\n",
+    "# Display the first 10 records\n",
+    "for i in range(10):\n",
+    "    print(f\"Record {i+1}:\")\n",
+    "    print(f\"Image:\\n{x_train[i]}\")\n",
+    "    print(f\"Label: {y_train[i]}\")\n",
+    "    print(\"\\n\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting tensorflow\n",
+      "  Downloading tensorflow-2.16.2-cp310-cp310-macosx_12_0_arm64.whl (227.0 MB)\n",
+      "\u001b[K     |████████████████████████████████| 227.0 MB 50.3 MB/s eta 0:00:01\n",
+      "\u001b[?25hCollecting h5py>=3.10.0\n",
+      "  Using cached h5py-3.11.0-cp310-cp310-macosx_11_0_arm64.whl (2.9 MB)\n",
+      "Collecting libclang>=13.0.0\n",
+      "  Using cached libclang-18.1.1-py2.py3-none-macosx_11_0_arm64.whl (26.4 MB)\n",
+      "Requirement already satisfied: opt-einsum>=2.3.2 in /Users/nihar/.pyenv/versions/3.10.0/lib/python3.10/site-packages (from tensorflow) (3.3.0)\n",
+      "Collecting astunparse>=1.6.0\n",
+      "  Using cached astunparse-1.6.3-py2.py3-none-any.whl (12 kB)\n",
+      "Collecting keras>=3.0.0\n",
+      "  Downloading keras-3.4.1-py3-none-any.whl (1.1 MB)\n",
+      "\u001b[K     |████████████████████████████████| 1.1 MB 23.7 MB/s eta 0:00:01\n",
+      "\u001b[?25hCollecting ml-dtypes~=0.3.1\n",
+      "  Using cached ml_dtypes-0.3.2-cp310-cp310-macosx_10_9_universal2.whl (389 kB)\n",
+      "Requirement already satisfied: typing-extensions>=3.6.6 in /Users/nihar/.pyenv/versions/3.10.0/lib/python3.10/site-packages (from tensorflow) (4.11.0)\n",
+      "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /Users/nihar/.pyenv/versions/3.10.0/lib/python3.10/site-packages (from tensorflow) (1.64.1)\n",
+      "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /Users/nihar/.pyenv/versions/3.10.0/lib/python3.10/site-packages (from tensorflow) (3.20.3)\n",
+      "Requirement already satisfied: six>=1.12.0 in /Users/nihar/.pyenv/versions/3.10.0/lib/python3.10/site-packages (from tensorflow) (1.16.0)\n",
+      "Requirement already satisfied: absl-py>=1.0.0 in /Users/nihar/.pyenv/versions/3.10.0/lib/python3.10/site-packages (from tensorflow) (2.1.0)\n",
+      "Collecting google-pasta>=0.1.1\n",
+      "  Using cached google_pasta-0.2.0-py3-none-any.whl (57 kB)\n",
+      "Collecting tensorboard<2.17,>=2.16\n",
+      "  Using cached tensorboard-2.16.2-py3-none-any.whl (5.5 MB)\n",
+      "Requirement already satisfied: wrapt>=1.11.0 in /Users/nihar/.pyenv/versions/3.10.0/lib/python3.10/site-packages (from tensorflow) (1.16.0)\n",
+      "Requirement already satisfied: numpy<2.0.0,>=1.23.5 in /Users/nihar/.pyenv/versions/3.10.0/lib/python3.10/site-packages (from tensorflow) (1.26.4)\n",
+      "Requirement already satisfied: setuptools in /Users/nihar/.pyenv/versions/3.10.0/lib/python3.10/site-packages (from tensorflow) (70.1.0)\n",
+      "Requirement already satisfied: termcolor>=1.1.0 in /Users/nihar/.pyenv/versions/3.10.0/lib/python3.10/site-packages (from tensorflow) (2.4.0)\n",
+      "Collecting tensorflow-io-gcs-filesystem>=0.23.1\n",
+      "  Using cached tensorflow_io_gcs_filesystem-0.37.0-cp310-cp310-macosx_12_0_arm64.whl (3.5 MB)\n",
+      "Requirement already satisfied: flatbuffers>=23.5.26 in /Users/nihar/.pyenv/versions/3.10.0/lib/python3.10/site-packages (from tensorflow) (24.3.25)\n",
+      "Requirement already satisfied: packaging in /Users/nihar/.pyenv/versions/3.10.0/lib/python3.10/site-packages (from tensorflow) (24.0)\n",
+      "Collecting gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1\n",
+      "  Downloading gast-0.6.0.tar.gz (27 kB)\n",
+      "Requirement already satisfied: requests<3,>=2.21.0 in /Users/nihar/.pyenv/versions/3.10.0/lib/python3.10/site-packages (from tensorflow) (2.32.3)\n",
+      "Collecting wheel<1.0,>=0.23.0\n",
+      "  Using cached wheel-0.43.0-py3-none-any.whl (65 kB)\n",
+      "Requirement already satisfied: rich in /Users/nihar/.pyenv/versions/3.10.0/lib/python3.10/site-packages (from keras>=3.0.0->tensorflow) (13.7.1)\n",
+      "Collecting optree\n",
+      "  Using cached optree-0.11.0-cp310-cp310-macosx_11_0_arm64.whl (273 kB)\n",
+      "Collecting namex\n",
+      "  Using cached namex-0.0.8-py3-none-any.whl (5.8 kB)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /Users/nihar/.pyenv/versions/3.10.0/lib/python3.10/site-packages (from requests<3,>=2.21.0->tensorflow) (3.3.2)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/nihar/.pyenv/versions/3.10.0/lib/python3.10/site-packages (from requests<3,>=2.21.0->tensorflow) (2.2.2)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /Users/nihar/.pyenv/versions/3.10.0/lib/python3.10/site-packages (from requests<3,>=2.21.0->tensorflow) (2024.6.2)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /Users/nihar/.pyenv/versions/3.10.0/lib/python3.10/site-packages (from requests<3,>=2.21.0->tensorflow) (3.7)\n",
+      "Requirement already satisfied: werkzeug>=1.0.1 in /Users/nihar/.pyenv/versions/3.10.0/lib/python3.10/site-packages (from tensorboard<2.17,>=2.16->tensorflow) (2.2.3)\n",
+      "Collecting tensorboard-data-server<0.8.0,>=0.7.0\n",
+      "  Using cached tensorboard_data_server-0.7.2-py3-none-any.whl (2.4 kB)\n",
+      "Collecting markdown>=2.6.8\n",
+      "  Using cached Markdown-3.6-py3-none-any.whl (105 kB)\n",
+      "Requirement already satisfied: MarkupSafe>=2.1.1 in /Users/nihar/.pyenv/versions/3.10.0/lib/python3.10/site-packages (from werkzeug>=1.0.1->tensorboard<2.17,>=2.16->tensorflow) (2.1.5)\n",
+      "Requirement already satisfied: markdown-it-py>=2.2.0 in /Users/nihar/.pyenv/versions/3.10.0/lib/python3.10/site-packages (from rich->keras>=3.0.0->tensorflow) (3.0.0)\n",
+      "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /Users/nihar/.pyenv/versions/3.10.0/lib/python3.10/site-packages (from rich->keras>=3.0.0->tensorflow) (2.17.2)\n",
+      "Requirement already satisfied: mdurl~=0.1 in /Users/nihar/.pyenv/versions/3.10.0/lib/python3.10/site-packages (from markdown-it-py>=2.2.0->rich->keras>=3.0.0->tensorflow) (0.1.2)\n",
+      "Using legacy 'setup.py install' for gast, since package 'wheel' is not installed.\n",
+      "Installing collected packages: wheel, tensorboard-data-server, optree, namex, ml-dtypes, markdown, h5py, tensorflow-io-gcs-filesystem, tensorboard, libclang, keras, google-pasta, gast, astunparse, tensorflow\n",
+      "  Attempting uninstall: ml-dtypes\n",
+      "    Found existing installation: ml-dtypes 0.4.0\n",
+      "    Uninstalling ml-dtypes-0.4.0:\n",
+      "      Successfully uninstalled ml-dtypes-0.4.0\n",
+      "    Running setup.py install for gast ... \u001b[?25ldone\n",
+      "\u001b[?25hSuccessfully installed astunparse-1.6.3 gast-0.6.0 google-pasta-0.2.0 h5py-3.11.0 keras-3.4.1 libclang-18.1.1 markdown-3.6 ml-dtypes-0.3.2 namex-0.0.8 optree-0.11.0 tensorboard-2.16.2 tensorboard-data-server-0.7.2 tensorflow-2.16.2 tensorflow-io-gcs-filesystem-0.37.0 wheel-0.43.0\n",
+      "\u001b[33mWARNING: You are using pip version 21.2.3; however, version 24.1.1 is available.\n",
+      "You should consider upgrading via the '/Users/nihar/.pyenv/versions/3.10.0/bin/python3.10 -m pip install --upgrade pip' command.\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install tensorflow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zsh:1: unmatched \"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/nihar/.pyenv/versions/3.10.0/lib/python3.10/pty.py:89: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
+      "  pid, fd = os.forkpty()\n"
+     ]
+    }
+   ],
+   "source": [
+    "!streamlit run /Users/nihar/Desktop/Desktop - nihar’s MacBook Air/cc/code files/auto-ML/NNbasics/code.py\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'Python 3.12.3' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/opt/homebrew/bin/python3 -m pip install ipykernel -U --user --force-reinstall'"
+     ]
+    }
+   ],
+   "source": [
+    "!streamlit run \"/Users/nihar/Desktop/Desktop - nihar’s MacBook Air/cc/code files/auto-ML/NNbasics/code.py\""
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/gitflow.png b/gitflow.png
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diff --git a/modelllls/mnist_model_lr0.00010699_bs16_epochs10.json b/modelllls/mnist_model_lr0.00010699_bs16_epochs10.json
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diff --git a/modelllls/mnist_model_lr0.00010699_bs16_epochs30.json b/modelllls/mnist_model_lr0.00010699_bs16_epochs30.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr0.00010699_bs256_epochs15.json b/modelllls/mnist_model_lr0.00010699_bs256_epochs15.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr0.00010699_bs256_epochs35.json b/modelllls/mnist_model_lr0.00010699_bs256_epochs35.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr0.00010699_bs512_epochs45.json b/modelllls/mnist_model_lr0.00010699_bs512_epochs45.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr0.00012839_bs16_epochs50.json b/modelllls/mnist_model_lr0.00012839_bs16_epochs50.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr0.00012839_bs32_epochs20.json b/modelllls/mnist_model_lr0.00012839_bs32_epochs20.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr0.00012839_bs64_epochs25.json b/modelllls/mnist_model_lr0.00012839_bs64_epochs25.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr0.00015407_bs16_epochs10.json b/modelllls/mnist_model_lr0.00015407_bs16_epochs10.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr0.00015407_bs16_epochs30.json b/modelllls/mnist_model_lr0.00015407_bs16_epochs30.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr0.00015407_bs512_epochs50.json b/modelllls/mnist_model_lr0.00015407_bs512_epochs50.json
new file mode 100644
index 0000000000000000000000000000000000000000..000ac55596ae98d340d8aface667e716b54df6c1
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diff --git a/modelllls/mnist_model_lr0.00015407_bs8_epochs45.json b/modelllls/mnist_model_lr0.00015407_bs8_epochs45.json
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diff --git a/modelllls/mnist_model_lr0.00018488_bs128_epochs20.json b/modelllls/mnist_model_lr0.00018488_bs128_epochs20.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr0.00018488_bs16_epochs20.json b/modelllls/mnist_model_lr0.00018488_bs16_epochs20.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr0.00018488_bs32_epochs50.json b/modelllls/mnist_model_lr0.00018488_bs32_epochs50.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr0.00018488_bs512_epochs25.json b/modelllls/mnist_model_lr0.00018488_bs512_epochs25.json
new file mode 100644
index 0000000000000000000000000000000000000000..80b6ddc6cf3951794cc091dd86bfc6469c31e300
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diff --git a/modelllls/mnist_model_lr0.00018488_bs8_epochs15.json b/modelllls/mnist_model_lr0.00018488_bs8_epochs15.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr0.00018488_bs8_epochs35.json b/modelllls/mnist_model_lr0.00018488_bs8_epochs35.json
new file mode 100644
index 0000000000000000000000000000000000000000..71be1bc9f3f37e08e2db7937ff7360300eaf26b5
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diff --git a/modelllls/mnist_model_lr0.00022186_bs16_epochs40.json b/modelllls/mnist_model_lr0.00022186_bs16_epochs40.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr0.00022186_bs256_epochs20.json b/modelllls/mnist_model_lr0.00022186_bs256_epochs20.json
new file mode 100644
index 0000000000000000000000000000000000000000..fd169e5478281835a90a4b1b7c3517e25d050fbe
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diff --git a/modelllls/mnist_model_lr0.00022186_bs32_epochs10.json b/modelllls/mnist_model_lr0.00022186_bs32_epochs10.json
new file mode 100644
index 0000000000000000000000000000000000000000..82f4a1925d90d2655f589b2af6fe79608d938fd2
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diff --git a/modelllls/mnist_model_lr0.00022186_bs512_epochs50.json b/modelllls/mnist_model_lr0.00022186_bs512_epochs50.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr0.00022186_bs64_epochs15.json b/modelllls/mnist_model_lr0.00022186_bs64_epochs15.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr0.00022186_bs64_epochs35.json b/modelllls/mnist_model_lr0.00022186_bs64_epochs35.json
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diff --git a/modelllls/mnist_model_lr0.00026623_bs256_epochs35.json b/modelllls/mnist_model_lr0.00026623_bs256_epochs35.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr0.00026623_bs64_epochs20.json b/modelllls/mnist_model_lr0.00026623_bs64_epochs20.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr0.00031948_bs16_epochs20.json b/modelllls/mnist_model_lr0.00031948_bs16_epochs20.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr0.00031948_bs256_epochs20.json b/modelllls/mnist_model_lr0.00031948_bs256_epochs20.json
new file mode 100644
index 0000000000000000000000000000000000000000..1de9d31a899abd6271824cb5ec6226ad13bbca14
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diff --git a/modelllls/mnist_model_lr0.00031948_bs32_epochs50.json b/modelllls/mnist_model_lr0.00031948_bs32_epochs50.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr0.00031948_bs512_epochs50.json b/modelllls/mnist_model_lr0.00031948_bs512_epochs50.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr0.00031948_bs8_epochs25.json b/modelllls/mnist_model_lr0.00031948_bs8_epochs25.json
new file mode 100644
index 0000000000000000000000000000000000000000..7cbe826489fd580c1aa0499b88d8e39cb8721d09
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diff --git a/modelllls/mnist_model_lr1.2e-05_bs128_epochs15.json b/modelllls/mnist_model_lr1.2e-05_bs128_epochs15.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr1.2e-05_bs128_epochs35.json b/modelllls/mnist_model_lr1.2e-05_bs128_epochs35.json
new file mode 100644
index 0000000000000000000000000000000000000000..4420d456a51e1b185cdfcd104aa1e5ff7e9af983
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diff --git a/modelllls/mnist_model_lr1.2e-05_bs16_epochs45.json b/modelllls/mnist_model_lr1.2e-05_bs16_epochs45.json
new file mode 100644
index 0000000000000000000000000000000000000000..424fe64f9fb3bd94f2874b4e002fbe2137735c1f
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diff --git a/modelllls/mnist_model_lr1.2e-05_bs256_epochs40.json b/modelllls/mnist_model_lr1.2e-05_bs256_epochs40.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr1.2e-05_bs32_epochs15.json b/modelllls/mnist_model_lr1.2e-05_bs32_epochs15.json
new file mode 100644
index 0000000000000000000000000000000000000000..4313eaddf21161edad3e2d9a35f46f529ac219ed
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diff --git a/modelllls/mnist_model_lr1.2e-05_bs512_epochs10.json b/modelllls/mnist_model_lr1.2e-05_bs512_epochs10.json
new file mode 100644
index 0000000000000000000000000000000000000000..32007506f1b28d82b26a333febc1ad6ce85c4cd8
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diff --git a/modelllls/mnist_model_lr1.2e-05_bs512_epochs30.json b/modelllls/mnist_model_lr1.2e-05_bs512_epochs30.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr1.2e-05_bs64_epochs10.json b/modelllls/mnist_model_lr1.2e-05_bs64_epochs10.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr1.2e-05_bs8_epochs45.json b/modelllls/mnist_model_lr1.2e-05_bs8_epochs45.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr1.44e-05_bs128_epochs35.json b/modelllls/mnist_model_lr1.44e-05_bs128_epochs35.json
new file mode 100644
index 0000000000000000000000000000000000000000..1685386654e3433ec440525ab826de6d25fcf377
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diff --git a/modelllls/mnist_model_lr1.44e-05_bs256_epochs40.json b/modelllls/mnist_model_lr1.44e-05_bs256_epochs40.json
new file mode 100644
index 0000000000000000000000000000000000000000..16ed778a8027d08402010ff2f3938234c5fabdbb
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diff --git a/modelllls/mnist_model_lr1.44e-05_bs32_epochs15.json b/modelllls/mnist_model_lr1.44e-05_bs32_epochs15.json
new file mode 100644
index 0000000000000000000000000000000000000000..4e56f1aa9c0ef2897e32ea124780cd2729e1b737
--- /dev/null
+++ b/modelllls/mnist_model_lr1.44e-05_bs32_epochs15.json
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diff --git a/modelllls/mnist_model_lr1.44e-05_bs32_epochs35.json b/modelllls/mnist_model_lr1.44e-05_bs32_epochs35.json
new file mode 100644
index 0000000000000000000000000000000000000000..a9bda836f63689e011206ed6fad70cca4508404a
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diff --git a/modelllls/mnist_model_lr1.44e-05_bs512_epochs10.json b/modelllls/mnist_model_lr1.44e-05_bs512_epochs10.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr1.44e-05_bs512_epochs30.json b/modelllls/mnist_model_lr1.44e-05_bs512_epochs30.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr1.44e-05_bs64_epochs30.json b/modelllls/mnist_model_lr1.44e-05_bs64_epochs30.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr1.44e-05_bs8_epochs25.json b/modelllls/mnist_model_lr1.44e-05_bs8_epochs25.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr1.728e-05_bs16_epochs10.json b/modelllls/mnist_model_lr1.728e-05_bs16_epochs10.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr1.728e-05_bs32_epochs40.json b/modelllls/mnist_model_lr1.728e-05_bs32_epochs40.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr1.728e-05_bs512_epochs50.json b/modelllls/mnist_model_lr1.728e-05_bs512_epochs50.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr1.728e-05_bs64_epochs45.json b/modelllls/mnist_model_lr1.728e-05_bs64_epochs45.json
new file mode 100644
index 0000000000000000000000000000000000000000..f7af81faef7596abd88a0b615d3e345562ac22cc
--- /dev/null
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diff --git a/modelllls/mnist_model_lr1.728e-05_bs8_epochs45.json b/modelllls/mnist_model_lr1.728e-05_bs8_epochs45.json
new file mode 100644
index 0000000000000000000000000000000000000000..ab8b8608ebb422b63873f3b08a30a9dfcb07cabd
--- /dev/null
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diff --git a/modelllls/mnist_model_lr1e-05_bs128_epochs25.json b/modelllls/mnist_model_lr1e-05_bs128_epochs25.json
new file mode 100644
index 0000000000000000000000000000000000000000..474a27328b56e38c068cfcc9730de369cc4ea964
--- /dev/null
+++ b/modelllls/mnist_model_lr1e-05_bs128_epochs25.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/modelllls/mnist_model_lr1e-05_bs16_epochs50.json b/modelllls/mnist_model_lr1e-05_bs16_epochs50.json
new file mode 100644
index 0000000000000000000000000000000000000000..e4d3b624135443b0e219539fb40a7611507034d8
--- /dev/null
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diff --git a/modelllls/mnist_model_lr1e-05_bs256_epochs50.json b/modelllls/mnist_model_lr1e-05_bs256_epochs50.json
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diff --git a/modelllls/mnist_model_lr1e-05_bs32_epochs20.json b/modelllls/mnist_model_lr1e-05_bs32_epochs20.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr1e-05_bs512_epochs20.json b/modelllls/mnist_model_lr1e-05_bs512_epochs20.json
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diff --git a/modelllls/mnist_model_lr1e-05_bs64_epochs25.json b/modelllls/mnist_model_lr1e-05_bs64_epochs25.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr1e-05_bs8_epochs25.json b/modelllls/mnist_model_lr1e-05_bs8_epochs25.json
new file mode 100644
index 0000000000000000000000000000000000000000..65600ab988459c40c7bb92355c41bd0c859f758c
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diff --git a/modelllls/mnist_model_lr2.074e-05_bs16_epochs25.json b/modelllls/mnist_model_lr2.074e-05_bs16_epochs25.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr2.074e-05_bs512_epochs25.json b/modelllls/mnist_model_lr2.074e-05_bs512_epochs25.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr2.074e-05_bs8_epochs50.json b/modelllls/mnist_model_lr2.074e-05_bs8_epochs50.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr2.488e-05_bs128_epochs10.json b/modelllls/mnist_model_lr2.488e-05_bs128_epochs10.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr2.488e-05_bs128_epochs30.json b/modelllls/mnist_model_lr2.488e-05_bs128_epochs30.json
new file mode 100644
index 0000000000000000000000000000000000000000..d844ae8098a2121ab11b3943d652e5f8e9876449
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diff --git a/modelllls/mnist_model_lr2.488e-05_bs256_epochs45.json b/modelllls/mnist_model_lr2.488e-05_bs256_epochs45.json
new file mode 100644
index 0000000000000000000000000000000000000000..573b9cb17962fcbde3bbe99f92439a2fd6ddeeff
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diff --git a/modelllls/mnist_model_lr2.488e-05_bs512_epochs15.json b/modelllls/mnist_model_lr2.488e-05_bs512_epochs15.json
new file mode 100644
index 0000000000000000000000000000000000000000..6a67607d1bbb18dc94519450ca90bdcfd9cf4e98
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diff --git a/modelllls/mnist_model_lr2.488e-05_bs512_epochs35.json b/modelllls/mnist_model_lr2.488e-05_bs512_epochs35.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr2.488e-05_bs64_epochs20.json b/modelllls/mnist_model_lr2.488e-05_bs64_epochs20.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr2.488e-05_bs8_epochs10.json b/modelllls/mnist_model_lr2.488e-05_bs8_epochs10.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr2.986e-05_bs32_epochs15.json b/modelllls/mnist_model_lr2.986e-05_bs32_epochs15.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr2.986e-05_bs32_epochs35.json b/modelllls/mnist_model_lr2.986e-05_bs32_epochs35.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr2.986e-05_bs64_epochs10.json b/modelllls/mnist_model_lr2.986e-05_bs64_epochs10.json
new file mode 100644
index 0000000000000000000000000000000000000000..59c92bd3a2cf4e7aadeed18e2ccf38949ae87d80
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diff --git a/modelllls/mnist_model_lr2.986e-05_bs64_epochs30.json b/modelllls/mnist_model_lr2.986e-05_bs64_epochs30.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr2.986e-05_bs8_epochs15.json b/modelllls/mnist_model_lr2.986e-05_bs8_epochs15.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr2.986e-05_bs8_epochs35.json b/modelllls/mnist_model_lr2.986e-05_bs8_epochs35.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr3.583e-05_bs512_epochs50.json b/modelllls/mnist_model_lr3.583e-05_bs512_epochs50.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr3.583e-05_bs64_epochs50.json b/modelllls/mnist_model_lr3.583e-05_bs64_epochs50.json
new file mode 100644
index 0000000000000000000000000000000000000000..b371fa93c222f6feb01d2da2459fbf6a7df0c01b
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diff --git a/modelllls/mnist_model_lr4.3e-05_bs16_epochs45.json b/modelllls/mnist_model_lr4.3e-05_bs16_epochs45.json
new file mode 100644
index 0000000000000000000000000000000000000000..ea18598c89badfb1fdbd5a38f8af5542f71a9120
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diff --git a/modelllls/mnist_model_lr4.3e-05_bs32_epochs35.json b/modelllls/mnist_model_lr4.3e-05_bs32_epochs35.json
new file mode 100644
index 0000000000000000000000000000000000000000..066647712f7ca941c31ee2c5976f1a1478bd637a
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diff --git a/modelllls/mnist_model_lr4.3e-05_bs64_epochs10.json b/modelllls/mnist_model_lr4.3e-05_bs64_epochs10.json
new file mode 100644
index 0000000000000000000000000000000000000000..d9d44bf6723f125e81474bed840abef402e0399d
--- /dev/null
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diff --git a/modelllls/mnist_model_lr4.3e-05_bs64_epochs30.json b/modelllls/mnist_model_lr4.3e-05_bs64_epochs30.json
new file mode 100644
index 0000000000000000000000000000000000000000..567071d8df1c2b5e08721774c034d22e0d455736
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diff --git a/modelllls/mnist_model_lr4.3e-05_bs8_epochs45.json b/modelllls/mnist_model_lr4.3e-05_bs8_epochs45.json
new file mode 100644
index 0000000000000000000000000000000000000000..8f60c4558b7525e527fdba8066ac7d8072e92068
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diff --git a/modelllls/mnist_model_lr5.16e-05_bs512_epochs50.json b/modelllls/mnist_model_lr5.16e-05_bs512_epochs50.json
new file mode 100644
index 0000000000000000000000000000000000000000..5e98dce1bba8ff587cf06261765aca20e5b45461
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diff --git a/modelllls/mnist_model_lr6.192e-05_bs128_epochs45.json b/modelllls/mnist_model_lr6.192e-05_bs128_epochs45.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr6.192e-05_bs16_epochs45.json b/modelllls/mnist_model_lr6.192e-05_bs16_epochs45.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr6.192e-05_bs256_epochs30.json b/modelllls/mnist_model_lr6.192e-05_bs256_epochs30.json
new file mode 100644
index 0000000000000000000000000000000000000000..ee5aeb351a40e142c4370dd69aa342bc19f8449b
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diff --git a/modelllls/mnist_model_lr6.192e-05_bs32_epochs15.json b/modelllls/mnist_model_lr6.192e-05_bs32_epochs15.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr6.192e-05_bs512_epochs40.json b/modelllls/mnist_model_lr6.192e-05_bs512_epochs40.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr6.192e-05_bs64_epochs30.json b/modelllls/mnist_model_lr6.192e-05_bs64_epochs30.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr7.43e-05_bs128_epochs15.json b/modelllls/mnist_model_lr7.43e-05_bs128_epochs15.json
new file mode 100644
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--- /dev/null
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diff --git a/modelllls/mnist_model_lr7.43e-05_bs128_epochs35.json b/modelllls/mnist_model_lr7.43e-05_bs128_epochs35.json
new file mode 100644
index 0000000000000000000000000000000000000000..52a952db03797512cf16a42dafafe07f60e8ab30
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diff --git a/modelllls/mnist_model_lr7.43e-05_bs256_epochs40.json b/modelllls/mnist_model_lr7.43e-05_bs256_epochs40.json
new file mode 100644
index 0000000000000000000000000000000000000000..0d9941ed47766f37e75fc0c7767ac528b4806ccb
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diff --git a/modelllls/mnist_model_lr7.43e-05_bs32_epochs40.json b/modelllls/mnist_model_lr7.43e-05_bs32_epochs40.json
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diff --git a/modelllls/mnist_model_lr7.43e-05_bs512_epochs10.json b/modelllls/mnist_model_lr7.43e-05_bs512_epochs10.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr7.43e-05_bs512_epochs30.json b/modelllls/mnist_model_lr7.43e-05_bs512_epochs30.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr7.43e-05_bs8_epochs45.json b/modelllls/mnist_model_lr7.43e-05_bs8_epochs45.json
new file mode 100644
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diff --git a/modelllls/mnist_model_lr8.916e-05_bs32_epochs10.json b/modelllls/mnist_model_lr8.916e-05_bs32_epochs10.json
new file mode 100644
index 0000000000000000000000000000000000000000..3118cfb8514195537331fbe34f9d04f55be89180
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diff --git a/modelllls/mnist_model_lr8.916e-05_bs32_epochs30.json b/modelllls/mnist_model_lr8.916e-05_bs32_epochs30.json
new file mode 100644
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+++ b/modelllls/mnist_model_lr8.916e-05_bs32_epochs30.json
@@ -0,0 +1 @@
+{"accuracy": [0.7492856979370117, 0.8874285817146301, 0.9079523682594299, 0.9179999828338623, 0.9243809580802917, 0.9300476312637329, 0.9350952506065369, 0.9390000104904175, 0.9432857036590576, 0.9464285969734192, 0.9494761824607849, 0.951714277267456, 0.9539999961853027, 0.9569047689437866, 0.9591904878616333, 0.9613809585571289, 0.9629523754119873, 0.9645714163780212, 0.9660000205039978, 0.9674761891365051, 0.9690476059913635, 0.9706190228462219, 0.9716190695762634, 0.9726190567016602, 0.9741904735565186, 0.9752857089042664, 0.9764285683631897, 0.9777143001556396, 0.9787619113922119, 0.9797142744064331], "loss": [1.0608561038970947, 0.4505597949028015, 0.3547946512699127, 0.3101769983768463, 0.2810339331626892, 0.25903213024139404, 0.24104510247707367, 0.2257179170846939, 0.21236096322536469, 0.2004222273826599, 0.18963965773582458, 0.17979155480861664, 0.17070011794567108, 0.1623266339302063, 0.15460622310638428, 0.14744138717651367, 0.140809565782547, 0.13460376858711243, 0.1287745237350464, 0.12332441657781601, 0.11819303035736084, 0.11335477977991104, 0.10880088806152344, 0.10447757691144943, 0.10037480294704437, 0.09645941853523254, 0.09271138161420822, 0.08913217484951019, 0.08571203052997589, 0.08244197815656662], "val_accuracy": [0.8711666464805603, 0.8966666460037231, 0.909500002861023, 0.9164999723434448, 0.9213333129882812, 0.9266666769981384, 0.9288333058357239, 0.9316666722297668, 0.9328333139419556, 0.934333324432373, 0.9356666803359985, 0.9366666674613953, 0.9393333196640015, 0.940500020980835, 0.9423333406448364, 0.9434999823570251, 0.9443333148956299, 0.9458333253860474, 0.9470000267028809, 0.9478333592414856, 0.9476666450500488, 0.9496666789054871, 0.9503333568572998, 0.9508333206176758, 0.9511666893959045, 0.9518333077430725, 0.9521666765213013, 0.9524999856948853, 0.952833354473114, 0.952833354473114], "val_loss": [0.5475695133209229, 0.3904932737350464, 0.3360939621925354, 0.3061428964138031, 0.286175936460495, 0.271225243806839, 0.2589590847492218, 0.248504638671875, 0.23957140743732452, 0.2316170483827591, 0.2244609147310257, 0.21791446208953857, 0.21190659701824188, 0.20636621117591858, 0.2014371156692505, 0.1968674659729004, 0.1926492303609848, 0.18861205875873566, 0.184940904378891, 0.18157319724559784, 0.17841394245624542, 0.1755198836326599, 0.1728871613740921, 0.17041562497615814, 0.16808900237083435, 0.16593341529369354, 0.16380824148654938, 0.161906898021698, 0.16005077958106995, 0.1583985537290573]}
\ No newline at end of file
diff --git a/modelllls/mnist_model_lr8.916e-05_bs64_epochs35.json b/modelllls/mnist_model_lr8.916e-05_bs64_epochs35.json
new file mode 100644
index 0000000000000000000000000000000000000000..6c2fd3801524134492a3d72c0b05c53b31e7a95c
--- /dev/null
+++ b/modelllls/mnist_model_lr8.916e-05_bs64_epochs35.json
@@ -0,0 +1 @@
+{"accuracy": [0.6592381000518799, 0.8620952367782593, 0.8902857303619385, 0.9053333401679993, 0.9132381081581116, 0.9195713996887207, 0.9244762063026428, 0.9289047718048096, 0.9329047799110413, 0.9359999895095825, 0.939047634601593, 0.941428542137146, 0.9443333148956299, 0.9462857246398926, 0.9487142562866211, 0.9506666660308838, 0.9522857069969177, 0.9539523720741272, 0.9559524059295654, 0.9570476412773132, 0.9585238099098206, 0.9595714211463928, 0.9612857103347778, 0.961809515953064, 0.9629047513008118, 0.9640476107597351, 0.9649999737739563, 0.9661904573440552, 0.9670952558517456, 0.9685238003730774, 0.9693809747695923, 0.9702380895614624, 0.9714285731315613, 0.972095251083374, 0.9734285473823547], "loss": [1.3925665616989136, 0.5977274179458618, 0.4351916015148163, 0.3683302402496338, 0.32910674810409546, 0.30199581384658813, 0.2812024652957916, 0.2641820013523102, 0.24971316754817963, 0.23726901412010193, 0.2262745052576065, 0.2164423167705536, 0.20749828219413757, 0.1993430107831955, 0.19180263578891754, 0.18477530777454376, 0.17819102108478546, 0.17201371490955353, 0.16616982221603394, 0.16064736247062683, 0.15539675951004028, 0.15037648379802704, 0.1456005722284317, 0.1410444974899292, 0.13670095801353455, 0.1325386017560959, 0.1285482496023178, 0.12471334636211395, 0.12104093283414841, 0.11752765625715256, 0.1141393706202507, 0.11086957901716232, 0.1077175885438919, 0.1046806052327156, 0.10175514966249466], "val_accuracy": [0.8288333415985107, 0.8805000185966492, 0.8989999890327454, 0.906499981880188, 0.9129999876022339, 0.9173333048820496, 0.921500027179718, 0.9238333106040955, 0.9269999861717224, 0.9293333292007446, 0.9315000176429749, 0.9336666464805603, 0.9351666569709778, 0.9380000233650208, 0.9394999742507935, 0.940500020980835, 0.9415000081062317, 0.9430000185966492, 0.9438333511352539, 0.9434999823570251, 0.9441666603088379, 0.9449999928474426, 0.9456666707992554, 0.9463333487510681, 0.9473333358764648, 0.9481666684150696, 0.9483333230018616, 0.9484999775886536, 0.9488333463668823, 0.9488333463668823, 0.950166642665863, 0.949833333492279, 0.949833333492279, 0.9503333568572998, 0.9509999752044678], "val_loss": [0.7732332944869995, 0.4883183538913727, 0.39725178480148315, 0.3506659269332886, 0.3216719627380371, 0.3012503683567047, 0.2855294346809387, 0.27280908823013306, 0.2621942460536957, 0.253184974193573, 0.2453787475824356, 0.2384902387857437, 0.23226845264434814, 0.2266097068786621, 0.2214825451374054, 0.21681515872478485, 0.2125377655029297, 0.20854710042476654, 0.20481200516223907, 0.20130720734596252, 0.19807279109954834, 0.1949959546327591, 0.1920272409915924, 0.18930456042289734, 0.18671301007270813, 0.18424300849437714, 0.1818656176328659, 0.1796160340309143, 0.17752636969089508, 0.17550183832645416, 0.17355281114578247, 0.17173804342746735, 0.1700252741575241, 0.16836822032928467, 0.16677697002887726]}
\ No newline at end of file
diff --git a/nnbasics.py b/nnbasics.py
new file mode 100644
index 0000000000000000000000000000000000000000..6111898548b08d228c715594974c534b837e7c6b
--- /dev/null
+++ b/nnbasics.py
@@ -0,0 +1,150 @@
+import streamlit as st
+import matplotlib.pyplot as plt
+import seaborn as sns
+import json
+import os
+import numpy as np
+from streamlit_lottie import st_lottie
+
+# Custom CSS to increase text size and beautify the app
+# Custom CSS to increase text size, beautify the app, and highlight the final message
+st.markdown("""
+    <style>
+    .big-font {
+        font-size:60px !important;
+        text-align: center;
+    }
+    .slider-label {
+        font-size:25px !important;
+        font-weight: bold;
+    }
+    .small-text {
+        font-size:12px !important;
+    }
+    .medium-text {
+        font-size:16px !important;
+    }
+    .center-text {
+        text-align: center;
+    }
+    .highlight {
+        font-family: 'Courier New', Courier, monospace;
+        background-color: #f0f0f0;
+        padding: 10px;
+        border-radius: 5px;
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
+# Load Lottie animation
+def load_lottiefile(filepath: str):
+    with open(filepath, "r") as f:
+        return json.load(f)
+
+# Load training history
+def load_history(history_path):
+    with open(history_path, 'r') as f:
+        history = json.load(f)
+    return history
+
+# Smooth data
+def smooth_data(data, window_size):
+    return np.convolve(data, np.ones(window_size)/window_size, mode='valid')
+
+# Streamlit app
+st.markdown('<h1 class="big-font">TuNNe</h1>', unsafe_allow_html=True)
+st.markdown('<h2 class="center-text">Tuning a Neural Network</h2>', unsafe_allow_html=True)
+st.markdown('<p class="center-text">This app demonstrates how different hyperparameters affect the training of a neural network using the MNIST dataset (50% of the data).</p>', unsafe_allow_html=True)
+
+# Load and display Lottie animation
+lottie_animation = load_lottiefile("Animation - 1719728959093.json")
+st_lottie(lottie_animation, height=300, key="header_animation")
+
+# Directory containing models
+model_dir = "modelllls"
+model_files = [f for f in os.listdir(model_dir) if f.endswith('.json')]
+
+# Extract available hyperparameters from model filenames
+def extract_hyperparameters(model_files):
+    hyperparameters = []
+    for f in model_files:
+        parts = f.split('_')
+        lr = float(parts[2][2:])
+        bs = int(parts[3][2:])
+        epochs = int(parts[4][6:].replace('.json', ''))
+        hyperparameters.append((lr, bs, epochs))
+    return hyperparameters
+
+hyperparameters = extract_hyperparameters(model_files)
+
+# Get unique values for each hyperparameter
+learning_rates = sorted(set(lr for lr, _, _ in hyperparameters))
+
+# Select slider for learning rate
+st.markdown('<p class="slider-label">Learning Rate</p>', unsafe_allow_html=True)
+selected_lr = st.select_slider("", options=learning_rates)
+
+# Filter batch sizes based on selected learning rate
+filtered_bs = sorted(set(bs for lr, bs, _ in hyperparameters if lr == selected_lr))
+st.markdown('<p class="slider-label">Batch Size</p>', unsafe_allow_html=True)
+selected_bs = st.select_slider("", options=filtered_bs)
+
+# Filter epochs based on selected learning rate and batch size
+filtered_epochs = sorted(set(epochs for lr, bs, epochs in hyperparameters if lr == selected_lr and bs == selected_bs))
+st.markdown('<p class="slider-label">Epochs</p>', unsafe_allow_html=True)
+selected_epochs = st.select_slider("", options=filtered_epochs)
+
+# Options for grid and smoothing
+enable_grid = st.checkbox("Enable Grid Lines")
+if selected_epochs > 20:
+    smoothing_window = st.slider("Smoothing Window (every 4 epochs)", min_value=1, max_value=5, step=1, value=1)
+
+# Find the corresponding history file
+history_filename = f"mnist_model_lr{selected_lr}_bs{selected_bs}_epochs{selected_epochs}.json"
+history_path = os.path.join(model_dir, history_filename)
+
+if os.path.exists(history_path):
+    history = load_history(history_path)
+
+    # Plot training & validation accuracy values
+    fig, ax = plt.subplots()
+    accuracy = history['accuracy']
+    val_accuracy = history['val_accuracy']
+    if selected_epochs > 20 and smoothing_window > 1:
+        accuracy = smooth_data(accuracy, smoothing_window * 4)
+        val_accuracy = smooth_data(val_accuracy, smoothing_window * 4)
+    sns.lineplot(x=range(len(accuracy)), y=accuracy, ax=ax, label='Train Accuracy')
+    sns.lineplot(x=range(len(val_accuracy)), y=val_accuracy, ax=ax, label='Validation Accuracy')
+    ax.set_title('Model Accuracy', fontsize=15)
+    ax.set_ylabel('Accuracy', fontsize=12)
+    ax.set_xlabel('Epoch', fontsize=12)
+    ax.legend(loc='upper left', fontsize=10)
+    if enable_grid:
+        ax.grid(True)
+    st.pyplot(fig)
+
+    # Plot training & validation loss values
+    fig, ax = plt.subplots()
+    loss = history['loss']
+    val_loss = history['val_loss']
+    if selected_epochs > 20 and smoothing_window > 1:
+        loss = smooth_data(loss, smoothing_window * 4)
+        val_loss = smooth_data(val_loss, smoothing_window * 4)
+    sns.lineplot(x=range(len(loss)), y=loss, ax=ax, label='Train Loss')
+    sns.lineplot(x=range(len(val_loss)), y=val_loss, ax=ax, label='Validation Loss')
+    ax.set_title('Model Loss', fontsize=15)
+    ax.set_ylabel('Loss', fontsize=12)
+    ax.set_xlabel('Epoch', fontsize=12)
+    ax.legend(loc='upper left', fontsize=10)
+    if enable_grid:
+        ax.grid(True)
+    st.pyplot(fig)
+else:
+    st.error(f"History file not found: {history_path}")
+
+
+# Final message
+st.markdown("""
+    <p class="medium-text">There is no rule of thumb for hyperparameters. The combination varies, and this is just to give an idea or an interactive way of showing how each parameter affects the model training.</p>
+    <p class="medium-text">Though the code is generated using AI, the tuning has to be done by a human. 😂</p>
+    """, unsafe_allow_html=True)
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..cce95243a68c44b00ea89a8189dafcecedb2eb03
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,5 @@
+streamlit
+matplotlib
+numpy
+seaborn
+streamlit_lottie
diff --git a/train.py b/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..ecd163a9a77086285d37b83fc26cdc1d2195d981
--- /dev/null
+++ b/train.py
@@ -0,0 +1,73 @@
+import os
+import random
+import tensorflow as tf
+from tensorflow.keras.datasets import mnist
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, Flatten
+from tensorflow.keras.optimizers import Adam
+import json
+from tqdm import tqdm
+
+# Set random seed for reproducibility
+random.seed(47)
+tf.random.set_seed(47)
+
+# Directory to save models
+model_dir = "/Users/nihar/Desktop/Desktop - nihar’s MacBook Air/cc/code files/auto-ML/NNbasics/modelllls"
+if not os.path.exists(model_dir):
+    os.makedirs(model_dir)
+
+# Clear the directory
+for f in os.listdir(model_dir):
+    os.remove(os.path.join(model_dir, f))
+
+# Load 50% of MNIST data
+(x_train_full, y_train_full), (x_test_full, y_test_full) = mnist.load_data()
+x_train_full, x_test_full = x_train_full / 255.0, x_test_full / 255.0
+
+# Use 50% of the data
+x_train_full, y_train_full = x_train_full[:30000], y_train_full[:30000]
+x_test_full, y_test_full = x_test_full[:5000], y_test_full[:5000]
+
+# Split into training, validation, and test sets
+train_size = int(0.7 * len(x_train_full))
+val_size = int(0.2 * len(x_train_full))
+test_size = len(x_train_full) - train_size - val_size
+
+x_train, y_train = x_train_full[:train_size], y_train_full[:train_size]
+x_val, y_val = x_train_full[train_size:train_size + val_size], y_train_full[train_size:train_size + val_size]
+x_test, y_test = x_train_full[train_size + val_size:], y_train_full[train_size + val_size:]
+
+# Generate more values for learning rates, batch sizes, and epochs
+learning_rates = [round(0.00001 * (1.2 ** i), 8) for i in range(20)]
+batch_sizes = [8, 16, 32, 64, 128, 256, 512]
+epochs_list = [10, 15, 20, 25, 30, 35, 40, 45, 50]
+
+# Generate 999 random combinations
+combinations = set()
+while len(combinations) < 999:
+    lr = random.choice(learning_rates)
+    bs = random.choice(batch_sizes)
+    epochs = random.choice(epochs_list)
+    combinations.add((lr, bs, epochs))
+
+# Train and save models with progress bar
+for i, (lr, bs, epochs) in enumerate(tqdm(combinations, desc="Training models")):
+    model = Sequential([
+        Flatten(input_shape=(28, 28)),
+        Dense(128, activation='relu'),
+        Dense(10, activation='softmax')
+    ])
+    
+    model.compile(optimizer=Adam(learning_rate=lr),
+                  loss='sparse_categorical_crossentropy',
+                  metrics=['accuracy'])
+    
+    history = model.fit(x_train, y_train, epochs=epochs, batch_size=bs, validation_data=(x_val, y_val), verbose=0)
+    
+    model_filename = f"mnist_model_lr{lr}_bs{bs}_epochs{epochs}.keras"
+    model.save(os.path.join(model_dir, model_filename))
+    
+    history_filename = model_filename.replace('.keras', '.json')
+    with open(os.path.join(model_dir, history_filename), 'w') as f:
+        json.dump(history.history, f)
\ No newline at end of file