diff --git a/Framework/Keras/Keras Assingment/YugalJain_ModelwithLogs.ipynb b/Framework/Keras/Keras Assingment/YugalJain_ModelwithLogs.ipynb
new file mode 100644
index 0000000..2fa06e8
--- /dev/null
+++ b/Framework/Keras/Keras Assingment/YugalJain_ModelwithLogs.ipynb	
@@ -0,0 +1,173 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\PC\\Anaconda3\\envs\\python35\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
+      "  from ._conv import register_converters as _register_converters\n",
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import keras\n",
+    "from keras import optimizers\n",
+    "from keras.models import Sequential\n",
+    "model=Sequential()\n",
+    "from keras.datasets import mnist\n",
+    "(x_train,y_train),(x_test,y_test)=mnist.load_data()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train=keras.utils.normalize(x_train,axis=1)\n",
+    "x_test=keras.utils.normalize(x_test,axis=1)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Error when checking target: expected dense_8 to have shape (10,) but got array with shape (1,)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-11-06fb33d73735>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      8\u001b[0m               metrics=['accuracy'])\n\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m32\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\envs\\python35\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[0;32m    950\u001b[0m             \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    951\u001b[0m             \u001b[0mclass_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 952\u001b[1;33m             batch_size=batch_size)\n\u001b[0m\u001b[0;32m    953\u001b[0m         \u001b[1;31m# Prepare validation data.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    954\u001b[0m         \u001b[0mdo_validation\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\envs\\python35\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36m_standardize_user_data\u001b[1;34m(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)\u001b[0m\n\u001b[0;32m    787\u001b[0m                 \u001b[0mfeed_output_shapes\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    788\u001b[0m                 \u001b[0mcheck_batch_axis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m  \u001b[1;31m# Don't enforce the batch size.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 789\u001b[1;33m                 exception_prefix='target')\n\u001b[0m\u001b[0;32m    790\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    791\u001b[0m             \u001b[1;31m# Generate sample-wise weight values given the `sample_weight` and\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\envs\\python35\\lib\\site-packages\\keras\\engine\\training_utils.py\u001b[0m in \u001b[0;36mstandardize_input_data\u001b[1;34m(data, names, shapes, check_batch_axis, exception_prefix)\u001b[0m\n\u001b[0;32m    136\u001b[0m                             \u001b[1;34m': expected '\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mnames\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m' to have shape '\u001b[0m \u001b[1;33m+\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    137\u001b[0m                             \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m' but got array with shape '\u001b[0m \u001b[1;33m+\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 138\u001b[1;33m                             str(data_shape))\n\u001b[0m\u001b[0;32m    139\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    140\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mValueError\u001b[0m: Error when checking target: expected dense_8 to have shape (10,) but got array with shape (1,)"
+     ]
+    }
+   ],
+   "source": [
+    "from keras.layers import Dense,Flatten\n",
+    "model=Sequential()\n",
+    "model.add(Flatten())\n",
+    "model.add(Dense(512,activation='relu'))\n",
+    "model.add(Dense(10,activation='softmax'))\n",
+    "model.compile(optimizer='rmsprop',\n",
+    "              loss='',\n",
+    "              metrics=['accuracy'])\n",
+    "\n",
+    "model.fit(x_train,y_train,epochs=10,batch_size=32,verbose=1)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.5.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Framework/Keras/Keras Assingment/YugalJain_ModelwithLogs/YugalJain_ModelwithLogs.ipynb b/Framework/Keras/Keras Assingment/YugalJain_ModelwithLogs/YugalJain_ModelwithLogs.ipynb
new file mode 100644
index 0000000..43a1501
--- /dev/null
+++ b/Framework/Keras/Keras Assingment/YugalJain_ModelwithLogs/YugalJain_ModelwithLogs.ipynb	
@@ -0,0 +1,335 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "YugalJain_ModelwithLogs.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.5.5"
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "FToxEB5V9y6b",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "acf96764-bb8f-48e6-97ef-3dc50522cca0"
+      },
+      "source": [
+        "import keras\n",
+        "from keras import optimizers\n",
+        "from keras.models import Sequential\n",
+        "from keras.datasets import mnist\n",
+        "(x_train,y_train),(x_test,y_test)=mnist.load_data()\n"
+      ],
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Using TensorFlow backend.\n"
+          ],
+          "name": "stderr"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "HxzF9klN9y6v",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# Normalizing input training and testing data\n",
+        "x_train=keras.utils.normalize(x_train,axis=1)\n",
+        "x_test=keras.utils.normalize(x_test,axis=1)\n",
+        "\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "uPZNHmtx9y7K",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "tOeoupjs9y7R",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 666
+        },
+        "outputId": "8e1596be-196b-427a-9ad6-61ea2978c52a"
+      },
+      "source": [
+        "from keras.layers import Dense,Flatten\n",
+        "# Creating an instance for sequential model\n",
+        "model=Sequential()\n",
+        "\n",
+        "# Adding flatten layer to convert 28*28 pixels into an array of size 784\n",
+        "model.add(Flatten())\n",
+        "\n",
+        "# Adding hidden dense layer of 512 units and relu activation function \n",
+        "model.add(Dense(512,activation='relu'))\n",
+        "\n",
+        "# Adding dense layer of 10 units with softmax activation function\n",
+        "\n",
+        "model.add(Dense(10,activation='softmax'))\n",
+        "\n",
+        "# Compiling our model with sgd as optimizer\n",
+        "model.compile(optimizer='sgd',\n",
+        "              loss='sparse_categorical_crossentropy',\n",
+        "              metrics=['accuracy'])\n",
+        "\n",
+        "model.fit(x_train,y_train,epochs=10,batch_size=32,verbose=1)\n",
+        "\n",
+        "\n"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "WARNING: Logging before flag parsing goes to stderr.\n",
+            "W0713 11:02:35.218778 140436485834624 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
+            "\n",
+            "W0713 11:02:35.246967 140436485834624 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
+            "\n",
+            "W0713 11:02:35.268220 140436485834624 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
+            "\n",
+            "W0713 11:02:35.281612 140436485834624 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
+            "\n",
+            "W0713 11:02:35.316654 140436485834624 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3341: The name tf.log is deprecated. Please use tf.math.log instead.\n",
+            "\n",
+            "W0713 11:02:35.408233 140436485834624 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
+            "Instructions for updating:\n",
+            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
+            "W0713 11:02:35.438150 140436485834624 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:986: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
+            "\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Epoch 1/10\n",
+            "60000/60000 [==============================] - 11s 183us/step - loss: 1.0670 - acc: 0.7842\n",
+            "Epoch 2/10\n",
+            "60000/60000 [==============================] - 10s 174us/step - loss: 0.4594 - acc: 0.8799\n",
+            "Epoch 3/10\n",
+            "60000/60000 [==============================] - 10s 171us/step - loss: 0.3753 - acc: 0.8968\n",
+            "Epoch 4/10\n",
+            "60000/60000 [==============================] - 10s 169us/step - loss: 0.3370 - acc: 0.9049\n",
+            "Epoch 5/10\n",
+            "60000/60000 [==============================] - 11s 179us/step - loss: 0.3123 - acc: 0.9118\n",
+            "Epoch 6/10\n",
+            "60000/60000 [==============================] - 11s 177us/step - loss: 0.2939 - acc: 0.9176\n",
+            "Epoch 7/10\n",
+            "60000/60000 [==============================] - 10s 172us/step - loss: 0.2789 - acc: 0.9222\n",
+            "Epoch 8/10\n",
+            "60000/60000 [==============================] - 10s 171us/step - loss: 0.2658 - acc: 0.9258\n",
+            "Epoch 9/10\n",
+            "60000/60000 [==============================] - 11s 176us/step - loss: 0.2545 - acc: 0.9286\n",
+            "Epoch 10/10\n",
+            "60000/60000 [==============================] - 11s 176us/step - loss: 0.2442 - acc: 0.9315\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<keras.callbacks.History at 0x7fb9b0de1eb8>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Gcdf7QL69y7g",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 68
+        },
+        "outputId": "cad2aee7-ea1a-4d7d-95cb-ee3389d06be1"
+      },
+      "source": [
+        "#Evaluating the model on test dataset\n",
+        "loss, acc = model.evaluate(x_test, y_test)\n",
+        "print(\"Loss: \" + str(loss))\n",
+        "print(\"Accuarcy: \" + str(100*acc) + \"%\")"
+      ],
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "10000/10000 [==============================] - 1s 61us/step\n",
+            "Loss: 0.23936950590610503\n",
+            "Accuarcy: 93.24%\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "4EzjH1Cc9y7q",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "sRPmEoDz9y72",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "tEWCSqMO9y7-",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "gkwcYtzo9y8J",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Po1HrIxd9y8Y",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "1LA6Q5GT9y8k",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "grys8Ui19y81",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "nHRTV2409y89",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "UjHaC1Wh9y9J",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}