diff --git a/baselines/.ipynb_checkpoints/MNIST_baseline-checkpoint.ipynb b/baselines/.ipynb_checkpoints/MNIST_baseline-checkpoint.ipynb
index 58d307ae162a86fa6bdae50dab42a7cb8dcb8866..f7045c6dac50b106f3387a8e4ba985d7a56eb66c 100644
--- a/baselines/.ipynb_checkpoints/MNIST_baseline-checkpoint.ipynb
+++ b/baselines/.ipynb_checkpoints/MNIST_baseline-checkpoint.ipynb
@@ -142,23 +142,9 @@
},
{
"cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": true
- },
- "outputs": [
- {
- "ename": "NameError",
- "evalue": "name 'train_test_split' is not defined",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m<ipython-input-1-a7c485c3cf8f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_val\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
- "\u001b[0;31mNameError\u001b[0m: name 'train_test_split' is not defined"
- ]
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"X_train, X_val= train_test_split(train_data, test_size=0.2, random_state=42) "
]
@@ -263,7 +249,7 @@
"metadata": {},
"outputs": [],
"source": [
- "y_pred = classifier.predict(X_test)"
+ "y_pred = classifier.predict(X_val)"
]
},
{
@@ -277,7 +263,7 @@
},
{
"cell_type": "code",
- "execution_count": 115,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -289,20 +275,9 @@
},
{
"cell_type": "code",
- "execution_count": 116,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy of the model is : 0.89356210142727\n",
- "Recall of the model is : 0.89356210142727\n",
- "Precision of the model is : 0.89356210142727\n",
- "F1 score of the model is : 0.6058161925441079\n"
- ]
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"print(\"Accuracy of the model is :\" ,accuracy)\n",
"print(\"Recall of the model is :\" ,recall)\n",
@@ -325,10 +300,10 @@
"source": [
"import matplotlib.pyplot as plt\n",
"def correctly_predicted():\n",
- " correct_pred = np.where(y_pred == y_test)\n",
+ " correct_pred = np.where(y_pred == y_val)\n",
" fig = plt.figure()\n",
" for i in range(1,10):\n",
- " img = X_test.iloc[i,:]\n",
+ " img = X_val.iloc[i,:]\n",
" img = np.array(img).reshape(28,28)\n",
" fig.add_subplot(3,3,i)\n",
" plt.imshow(img,cmap='gray')\n",
@@ -352,12 +327,12 @@
},
{
"cell_type": "code",
- "execution_count": 117,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"final_test_path = \"test.csv\"\n",
- "final_test = pd.read_csv(final_test_path)"
+ "final_test = pd.read_csv(final_test_path,header=None)"
]
},
{
diff --git a/baselines/MNIST_baseline.ipynb b/baselines/MNIST_baseline.ipynb
index 58d307ae162a86fa6bdae50dab42a7cb8dcb8866..f7045c6dac50b106f3387a8e4ba985d7a56eb66c 100644
--- a/baselines/MNIST_baseline.ipynb
+++ b/baselines/MNIST_baseline.ipynb
@@ -142,23 +142,9 @@
},
{
"cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": true
- },
- "outputs": [
- {
- "ename": "NameError",
- "evalue": "name 'train_test_split' is not defined",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m<ipython-input-1-a7c485c3cf8f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_val\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
- "\u001b[0;31mNameError\u001b[0m: name 'train_test_split' is not defined"
- ]
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"X_train, X_val= train_test_split(train_data, test_size=0.2, random_state=42) "
]
@@ -263,7 +249,7 @@
"metadata": {},
"outputs": [],
"source": [
- "y_pred = classifier.predict(X_test)"
+ "y_pred = classifier.predict(X_val)"
]
},
{
@@ -277,7 +263,7 @@
},
{
"cell_type": "code",
- "execution_count": 115,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -289,20 +275,9 @@
},
{
"cell_type": "code",
- "execution_count": 116,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy of the model is : 0.89356210142727\n",
- "Recall of the model is : 0.89356210142727\n",
- "Precision of the model is : 0.89356210142727\n",
- "F1 score of the model is : 0.6058161925441079\n"
- ]
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"print(\"Accuracy of the model is :\" ,accuracy)\n",
"print(\"Recall of the model is :\" ,recall)\n",
@@ -325,10 +300,10 @@
"source": [
"import matplotlib.pyplot as plt\n",
"def correctly_predicted():\n",
- " correct_pred = np.where(y_pred == y_test)\n",
+ " correct_pred = np.where(y_pred == y_val)\n",
" fig = plt.figure()\n",
" for i in range(1,10):\n",
- " img = X_test.iloc[i,:]\n",
+ " img = X_val.iloc[i,:]\n",
" img = np.array(img).reshape(28,28)\n",
" fig.add_subplot(3,3,i)\n",
" plt.imshow(img,cmap='gray')\n",
@@ -352,12 +327,12 @@
},
{
"cell_type": "code",
- "execution_count": 117,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"final_test_path = \"test.csv\"\n",
- "final_test = pd.read_csv(final_test_path)"
+ "final_test = pd.read_csv(final_test_path,header=None)"
]
},
{