{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-02-04T12:40:27.437013Z","iopub.execute_input":"2022-02-04T12:40:27.437284Z","iopub.status.idle":"2022-02-04T12:40:27.464710Z","shell.execute_reply.started":"2022-02-04T12:40:27.437210Z","shell.execute_reply":"2022-02-04T12:40:27.464278Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from __future__ import print_function\nimport keras\nfrom keras.datasets import cifar10\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Activation, Flatten\nfrom keras.layers import Conv2D, MaxPooling2D\nimport os\n\nimport numpy as np\n\nimport seaborn as sns\nimport matplotlib\nimport matplotlib.pyplot as plt\n\nfrom sklearn.metrics import confusion_matrix, classification_report\nimport itertools\n\n%matplotlib inline","metadata":{"execution":{"iopub.status.busy":"2022-02-04T12:40:27.465720Z","iopub.execute_input":"2022-02-04T12:40:27.465962Z","iopub.status.idle":"2022-02-04T12:40:33.575915Z","shell.execute_reply.started":"2022-02-04T12:40:27.465940Z","shell.execute_reply":"2022-02-04T12:40:33.575038Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import tensorflow as tf\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom tensorflow.keras import datasets,models,layers","metadata":{"execution":{"iopub.status.busy":"2022-02-04T12:40:33.576978Z","iopub.execute_input":"2022-02-04T12:40:33.577192Z","iopub.status.idle":"2022-02-04T12:40:33.906812Z","shell.execute_reply.started":"2022-02-04T12:40:33.577169Z","shell.execute_reply":"2022-02-04T12:40:33.906075Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data = tf.keras.datasets.cifar10","metadata":{"execution":{"iopub.status.busy":"2022-02-04T12:40:33.909063Z","iopub.execute_input":"2022-02-04T12:40:33.909307Z","iopub.status.idle":"2022-02-04T12:40:33.913273Z","shell.execute_reply.started":"2022-02-04T12:40:33.909277Z","shell.execute_reply":"2022-02-04T12:40:33.912347Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"(train_images, train_labels), (test_images, test_labels) = data.load_data()\n","metadata":{"execution":{"iopub.status.busy":"2022-02-04T12:40:33.914562Z","iopub.execute_input":"2022-02-04T12:40:33.914782Z","iopub.status.idle":"2022-02-04T12:40:39.536339Z","shell.execute_reply.started":"2022-02-04T12:40:33.914752Z","shell.execute_reply":"2022-02-04T12:40:39.535498Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.figure()\nplt.imshow(train_images[1])\nplt.colorbar()\nplt.grid(False)\n\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-02-04T12:40:39.537845Z","iopub.execute_input":"2022-02-04T12:40:39.538103Z","iopub.status.idle":"2022-02-04T12:40:39.753734Z","shell.execute_reply.started":"2022-02-04T12:40:39.538078Z","shell.execute_reply":"2022-02-04T12:40:39.752966Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_images, test_images = train_images / 255.0, test_images / 255.0\n","metadata":{"execution":{"iopub.status.busy":"2022-02-04T12:40:39.754665Z","iopub.execute_input":"2022-02-04T12:40:39.754839Z","iopub.status.idle":"2022-02-04T12:40:40.165428Z","shell.execute_reply.started":"2022-02-04T12:40:39.754815Z","shell.execute_reply":"2022-02-04T12:40:40.164560Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"classes = [\"airplane\",\"automobile\",\"bird\",\"cat\",\"deer\",\"dog\",\"frog\",\"horse\",\"ship\",\"truck\"]\n","metadata":{"execution":{"iopub.status.busy":"2022-02-04T12:40:40.166493Z","iopub.execute_input":"2022-02-04T12:40:40.166687Z","iopub.status.idle":"2022-02-04T12:40:40.171188Z","shell.execute_reply.started":"2022-02-04T12:40:40.166664Z","shell.execute_reply":"2022-02-04T12:40:40.170178Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#(X_train,Y_train),(X_test,Y_test)=datasets.cifar10.load_data()","metadata":{"execution":{"iopub.status.busy":"2022-02-04T12:40:40.172552Z","iopub.execute_input":"2022-02-04T12:40:40.172825Z","iopub.status.idle":"2022-02-04T12:40:40.184204Z","shell.execute_reply.started":"2022-02-04T12:40:40.172793Z","shell.execute_reply":"2022-02-04T12:40:40.183423Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"model_ann = models.Sequential([\n        layers.Flatten(input_shape=(32,32,3)),\n        layers.Dense(3000, activation='relu'),\n        layers.Dense(2000, activation='relu'),\n        layers.Dense(10, activation='softmax')    \n    ])\n\nmodel_ann.compile(optimizer='SGD',\n              loss='sparse_categorical_crossentropy',\n              metrics=['accuracy'])\n\nMODEL_1=model_ann.fit(\n    train_images, train_labels, epochs=10\n)\n\n","metadata":{"execution":{"iopub.status.busy":"2022-02-02T13:10:00.450671Z","iopub.execute_input":"2022-02-02T13:10:00.451658Z"}}},{"cell_type":"markdown","source":"model_ann = models.Sequential([\n        layers.Flatten(input_shape=(32,32,3)),\n        layers.Dense(3000, activation='relu'),\n        layers.Dense(2000, activation='relu'),\n        layers.Dense(10, activation='softmax')    \n    ])\n\nmodel_ann.compile(optimizer='SGD',\n              loss='sparse_categorical_crossentropy',\n              metrics=['accuracy'])\n\nMODEL_1=model_ann.fit(\n    test_images, test_labels, epochs=10\n)\n","metadata":{"execution":{"iopub.status.busy":"2022-02-04T11:15:26.951170Z","iopub.execute_input":"2022-02-04T11:15:26.951524Z","iopub.status.idle":"2022-02-04T11:17:21.887666Z","shell.execute_reply.started":"2022-02-04T11:15:26.951492Z","shell.execute_reply":"2022-02-04T11:17:21.886813Z"}}},{"cell_type":"markdown","source":"model_ann = models.Sequential([\n        layers.Flatten(input_shape=(32,32,3)),\n\n        layers.Dense(3000, activation='relu'),\n        layers.Dropout(0.3),\n\n        layers.Dense(2000, activation='relu'),\n        layers.Dropout(0.3),\n\n        layers.Dense(10, activation='softmax')    \n    ])\n\nmodel_ann.compile(optimizer='SGD',\n              loss='sparse_categorical_crossentropy',\n              metrics=['accuracy'])\n\nMODEL_2=model_ann.fit(\n    train_images, train_labels, epochs=10\n)\n","metadata":{"execution":{"iopub.status.busy":"2022-02-01T16:10:45.555035Z","iopub.execute_input":"2022-02-01T16:10:45.555381Z","iopub.status.idle":"2022-02-01T16:20:55.443512Z","shell.execute_reply.started":"2022-02-01T16:10:45.555345Z","shell.execute_reply":"2022-02-01T16:20:55.44256Z"}}},{"cell_type":"markdown","source":"model_ann = models.Sequential([\n        layers.Flatten(input_shape=(32,32,3)),\n\n        layers.Dense(3000, activation='relu'),\n        layers.Dropout(0.3),\n\n        layers.Dense(2000, activation='relu'),\n        layers.Dropout(0.3),\n\n        layers.Dense(10, activation='softmax')    \n    ])\n\nmodel_ann.compile(optimizer='SGD',\n              loss='sparse_categorical_crossentropy',\n              metrics=['accuracy'])\n\nMODEL_2=model_ann.fit(\n    test_images, test_labels, epochs=10\n)\n","metadata":{"execution":{"iopub.status.busy":"2022-02-04T11:45:54.748049Z","iopub.execute_input":"2022-02-04T11:45:54.748298Z","iopub.status.idle":"2022-02-04T11:47:44.443839Z","shell.execute_reply.started":"2022-02-04T11:45:54.748266Z","shell.execute_reply":"2022-02-04T11:47:44.443284Z"}}},{"cell_type":"markdown","source":"model = models.Sequential([\n    layers.Flatten(input_shape=(32,32,3)),\n\n    layers.Dense(5000, activation='relu'),\n    layers.Dense(4000, activation='relu'),\n    layers.Dense(3000, activation='relu'),\n    layers.Dense(2000, activation='relu'),\n    layers.Dense(10, activation='softmax')\n])\n          \nmodel.compile(optimizer='SGD',\n              loss='sparse_categorical_crossentropy',\n              metrics=['accuracy'])\n\nMODEL_1=model.fit(\n    train_images, train_labels, epochs=10\n)","metadata":{"execution":{"iopub.status.busy":"2022-02-01T16:22:43.579597Z","iopub.execute_input":"2022-02-01T16:22:43.57995Z","iopub.status.idle":"2022-02-01T16:56:41.11974Z","shell.execute_reply.started":"2022-02-01T16:22:43.579914Z","shell.execute_reply":"2022-02-01T16:56:41.118997Z"}}},{"cell_type":"markdown","source":"model = models.Sequential([\n    layers.Flatten(input_shape=(32,32,3)),\n    layers.Dense(600, activation='relu'),\n    layers.Dense(700, activation='relu'),\n    layers.Dense(800, activation='relu'),\n    layers.Dense(1000, activation='relu'),\n    layers.Dense(10, activation='softmax')\n])\n          \nmodel.compile(optimizer='SGD',\n              loss='sparse_categorical_crossentropy',\n              metrics=['accuracy'])\nMODEL_1=model.fit(\n    train_images, train_labels, epochs=10\n)","metadata":{"execution":{"iopub.status.busy":"2022-02-01T16:57:33.777559Z","iopub.execute_input":"2022-02-01T16:57:33.778668Z","iopub.status.idle":"2022-02-01T17:00:39.351049Z","shell.execute_reply.started":"2022-02-01T16:57:33.778621Z","shell.execute_reply":"2022-02-01T17:00:39.349993Z"}}},{"cell_type":"markdown","source":"model = models.Sequential([\n    layers.Flatten(input_shape=(32,32,3)),\n    layers.Dense(600, activation='relu'),\n    layers.Dense(700, activation='relu'),\n    layers.Dense(800, activation='relu'),\n    layers.Dense(1000, activation='relu'),\n    layers.Dense(10, activation='softmax')\n])\n          \nmodel.compile(optimizer='SGD',\n              loss='sparse_categorical_crossentropy',\n              metrics=['accuracy'])\nMODEL_1=model.fit(\n    test_images, test_labels, epochs=10\n)","metadata":{}},{"cell_type":"markdown","source":"model = models.Sequential([\n    layers.Flatten(input_shape=(32,32,3)),\n\n    layers.Dense(600, activation='relu'),\n    layers.Dropout(0.3),\n    layers.Dense(700, activation='relu'),\n    layers.Dropout(0.3),\n    layers.Dense(800, activation='relu'),\n    layers.Dropout(0.3),\n    layers.Dense(1000, activation='relu'),\n    layers.Dropout(0.3),\n    layers.Dense(10, activation='softmax')\n])\n          \nmodel.compile(optimizer='SGD',\n              loss='sparse_categorical_crossentropy',\n              metrics=['accuracy'])\n\nMODEL_1=model.fit(\n    train_images, train_labels, epochs=10\n)","metadata":{"execution":{"iopub.status.busy":"2022-02-01T17:00:53.317065Z","iopub.execute_input":"2022-02-01T17:00:53.317375Z","iopub.status.idle":"2022-02-01T17:04:29.59208Z","shell.execute_reply.started":"2022-02-01T17:00:53.317339Z","shell.execute_reply":"2022-02-01T17:04:29.591085Z"}}},{"cell_type":"markdown","source":"model = models.Sequential([\n    layers.Flatten(input_shape=(32,32,3)),\n\n    layers.Dense(5000, activation='relu'),\n    layers.Dropout(0.3),\n    layers.Dense(4000, activation='relu'),\n    layers.Dropout(0.3),\n    layers.Dense(3000, activation='relu'),\n    layers.Dropout(0.3),\n    layers.Dense(2000, activation='relu'),\n    layers.Dropout(0.3),\n    layers.Dense(10, activation='softmax')\n])\n          \nmodel.compile(optimizer='SGD',\n              loss='sparse_categorical_crossentropy',\n              metrics=['accuracy'])\n\nMODEL_1=model.fit(\n    train_images, train_labels, epochs=10\n)","metadata":{"execution":{"iopub.status.busy":"2022-02-01T17:05:15.255733Z","iopub.execute_input":"2022-02-01T17:05:15.256162Z","iopub.status.idle":"2022-02-01T17:41:11.925009Z","shell.execute_reply.started":"2022-02-01T17:05:15.256118Z","shell.execute_reply":"2022-02-01T17:41:11.924152Z"}}},{"cell_type":"markdown","source":"model_cnn = models.Sequential([\n        layers.Conv2D(filters=128,kernel_size=(3,3),activation='relu',input_shape=(32,32,3)),\n        layers.MaxPooling2D((2,2)),\n        \n        layers.Conv2D(filters=512,kernel_size=(3,3),activation='relu'),\n        layers.MaxPooling2D((2,2)),\n    \n        #layers.Conv2D(filters=128,kernel_size=(3,3),activation='relu'),\n        #layers.MaxPooling2D((2,2)),\n        \n        layers.Flatten(),\n        layers.Dense(512, activation='relu'),\n        layers.Dense(10, activation='softmax')    \n    ])","metadata":{"execution":{"iopub.status.busy":"2022-02-02T18:07:15.446606Z","iopub.execute_input":"2022-02-02T18:07:15.447372Z","iopub.status.idle":"2022-02-02T18:07:15.666308Z","shell.execute_reply.started":"2022-02-02T18:07:15.447338Z","shell.execute_reply":"2022-02-02T18:07:15.665687Z"}}},{"cell_type":"code","source":"model = models.Sequential([\n    layers.Flatten(input_shape=(32,32,3)),\n\n    layers.Dense(5000, activation='relu'),\n    layers.Dense(4000, activation='relu'),\n    layers.Dense(3000, activation='relu'),\n    layers.Dense(2000, activation='relu'),\n    layers.Dense(10, activation='softmax')\n])\n          \nmodel.compile(optimizer='SGD',\n              loss='sparse_categorical_crossentropy',\n              metrics=['accuracy'])\n\nMODEL_1=model.fit(\n    test_images, test_labels, epochs=10\n)","metadata":{"execution":{"iopub.status.busy":"2022-02-04T12:40:40.186473Z","iopub.execute_input":"2022-02-04T12:40:40.186702Z","iopub.status.idle":"2022-02-04T12:45:42.838676Z","shell.execute_reply.started":"2022-02-04T12:40:40.186670Z","shell.execute_reply":"2022-02-04T12:45:42.837564Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model = models.Sequential([ layers.Flatten(input_shape=(32,32,3)),\n\nlayers.Dense(5000, activation='relu'),\nlayers.Dropout(0.3),\nlayers.Dense(4000, activation='relu'),\nlayers.Dropout(0.3),\nlayers.Dense(3000, activation='relu'),\nlayers.Dropout(0.3),\nlayers.Dense(2000, activation='relu'),\nlayers.Dropout(0.3),\nlayers.Dense(10, activation='softmax')\n])\n\nmodel.compile(optimizer='SGD', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n\nMODEL_1=model.fit( test_images, test_labels, epochs=10 )","metadata":{"execution":{"iopub.status.busy":"2022-02-04T12:45:42.839667Z","iopub.execute_input":"2022-02-04T12:45:42.839863Z","iopub.status.idle":"2022-02-04T12:50:57.336518Z","shell.execute_reply.started":"2022-02-04T12:45:42.839836Z","shell.execute_reply":"2022-02-04T12:50:57.336097Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from tensorflow.keras.optimizers import RMSprop\n","metadata":{"execution":{"iopub.status.busy":"2022-02-04T12:50:57.338090Z","iopub.execute_input":"2022-02-04T12:50:57.338343Z","iopub.status.idle":"2022-02-04T12:50:57.342724Z","shell.execute_reply.started":"2022-02-04T12:50:57.338305Z","shell.execute_reply":"2022-02-04T12:50:57.341814Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"model_cnn.compile(\n                loss = 'sparse_categorical_crossentropy',\n               optimizer = RMSprop(learning_rate=1e-4),\n               metrics = ['accuracy'])\n\n\nMODEL_2=model_cnn.fit(train_images, train_labels,\n                        epochs=20,\n                      verbose=1)","metadata":{"execution":{"iopub.status.busy":"2022-02-02T18:08:16.931796Z","iopub.execute_input":"2022-02-02T18:08:16.932394Z","iopub.status.idle":"2022-02-02T18:36:01.061779Z","shell.execute_reply.started":"2022-02-02T18:08:16.932343Z","shell.execute_reply":"2022-02-02T18:36:01.060513Z"}}},{"cell_type":"markdown","source":"model_cnn.evaluate(train_images, train_labels)\n","metadata":{"execution":{"iopub.status.busy":"2022-02-02T18:08:03.290086Z","iopub.execute_input":"2022-02-02T18:08:03.290393Z","iopub.status.idle":"2022-02-02T18:08:03.319095Z","shell.execute_reply.started":"2022-02-02T18:08:03.290363Z","shell.execute_reply":"2022-02-02T18:08:03.31799Z"}}},{"cell_type":"markdown","source":"model_cnn.evaluate(test_images, test_labels)\n","metadata":{"execution":{"iopub.status.busy":"2022-02-03T07:23:10.829507Z","iopub.execute_input":"2022-02-03T07:23:10.82984Z","iopub.status.idle":"2022-02-03T07:23:11.175204Z","shell.execute_reply.started":"2022-02-03T07:23:10.829801Z","shell.execute_reply":"2022-02-03T07:23:11.173686Z"}}},{"cell_type":"markdown","source":"model_cnn = models.Sequential([\n        layers.Conv2D(filters=128,kernel_size=(3,3),activation='relu',input_shape=(32,32,3)),\n        layers.MaxPooling2D((2,2)),\n        \n        layers.Conv2D(filters=512,kernel_size=(3,3),activation='relu'),\n        layers.MaxPooling2D((2,2)),\n    \n          \n        layers.Flatten(),\n        layers.Dense(512, activation='relu'),\n        layers.Dense(10, activation='softmax')    \n    ])","metadata":{"execution":{"iopub.status.busy":"2022-02-02T18:07:15.966099Z","iopub.status.idle":"2022-02-02T18:07:15.966618Z","shell.execute_reply.started":"2022-02-02T18:07:15.966418Z","shell.execute_reply":"2022-02-02T18:07:15.966437Z"}}},{"cell_type":"markdown","source":"model_cnn.compile(\n                loss = 'sparse_categorical_crossentropy',\n               optimizer = RMSprop(learning_rate=1e-4),\n               metrics = ['accuracy'])\n\n\nMODEL_2=model_cnn.fit(train_images, train_labels,\n                        epochs=20,\n                      verbose=1)","metadata":{"execution":{"iopub.status.busy":"2022-02-02T15:19:36.90778Z","iopub.execute_input":"2022-02-02T15:19:36.908443Z","iopub.status.idle":"2022-02-02T17:26:48.763263Z","shell.execute_reply.started":"2022-02-02T15:19:36.908406Z","shell.execute_reply":"2022-02-02T17:26:48.762567Z"}}},{"cell_type":"markdown","source":"model_cnn.evaluate(train_images, train_labels)\n","metadata":{"execution":{"iopub.status.busy":"2022-02-02T18:07:15.967492Z","iopub.status.idle":"2022-02-02T18:07:15.96848Z","shell.execute_reply.started":"2022-02-02T18:07:15.968234Z","shell.execute_reply":"2022-02-02T18:07:15.968266Z"}}},{"cell_type":"markdown","source":"model_cnn.evaluate(test_images, test_labels)\n","metadata":{"execution":{"iopub.status.busy":"2022-02-02T18:07:15.969671Z","iopub.status.idle":"2022-02-02T18:07:15.970025Z","shell.execute_reply.started":"2022-02-02T18:07:15.969857Z","shell.execute_reply":"2022-02-02T18:07:15.96988Z"}}},{"cell_type":"markdown","source":"model_cnn = models.Sequential([\n        layers.Conv2D(filters=128,kernel_size=(3,3),activation='relu',input_shape=(32,32,3)),\n        layers.MaxPooling2D((2,2)),\n        \n        layers.Conv2D(filters=512,kernel_size=(3,3),activation='relu'),\n        layers.MaxPooling2D((2,2)),\n        #layers.Dropout(0.3),\n    \n\n        \n        layers.Flatten(),\n        layers.Dense(512, activation='relu'),\n        layers.Dense(10, activation='softmax')    \n    ])","metadata":{"execution":{"iopub.status.busy":"2022-02-03T07:23:34.152668Z","iopub.execute_input":"2022-02-03T07:23:34.153729Z","iopub.status.idle":"2022-02-03T07:23:34.350902Z","shell.execute_reply.started":"2022-02-03T07:23:34.153682Z","shell.execute_reply":"2022-02-03T07:23:34.349599Z"}}},{"cell_type":"markdown","source":"model_cnn.compile(\n                loss = 'sparse_categorical_crossentropy',\n               optimizer = RMSprop(learning_rate=1e-4),\n               metrics = ['accuracy'])\n\n\nMODEL_2=model_cnn.fit(train_images, train_labels,\n                        epochs=20,\n                      verbose=1)","metadata":{"execution":{"iopub.status.busy":"2022-02-03T07:23:41.329523Z","iopub.execute_input":"2022-02-03T07:23:41.329887Z","iopub.status.idle":"2022-02-03T09:24:05.656212Z","shell.execute_reply.started":"2022-02-03T07:23:41.329846Z","shell.execute_reply":"2022-02-03T09:24:05.654341Z"}}},{"cell_type":"markdown","source":"model_cnn.evaluate(train_images, train_labels)\n","metadata":{"execution":{"iopub.status.busy":"2022-02-03T09:25:30.37564Z","iopub.execute_input":"2022-02-03T09:25:30.376422Z","iopub.status.idle":"2022-02-03T09:27:27.279068Z","shell.execute_reply.started":"2022-02-03T09:25:30.376371Z","shell.execute_reply":"2022-02-03T09:27:27.278173Z"}}},{"cell_type":"markdown","source":"model_cnn.evaluate(test_images, test_labels)\n","metadata":{"execution":{"iopub.status.busy":"2022-02-03T09:27:43.132812Z","iopub.execute_input":"2022-02-03T09:27:43.133107Z","iopub.status.idle":"2022-02-03T09:28:06.83956Z","shell.execute_reply.started":"2022-02-03T09:27:43.133074Z","shell.execute_reply":"2022-02-03T09:28:06.838919Z"}}},{"cell_type":"code","source":"model_cnn = models.Sequential([\n        layers.Conv2D(filters=128,kernel_size=(3,3),activation='relu',input_shape=(32,32,3)),\n        layers.MaxPooling2D((2,2)),\n        \n        layers.Conv2D(filters=512,kernel_size=(3,3),activation='relu'),\n        layers.MaxPooling2D((2,2)),\n        layers.Dropout(0.3),\n    \n        layers.Conv2D(filters=128,kernel_size=(3,3),activation='relu'),\n        layers.MaxPooling2D((2,2)),\n        layers.Dropout(0.3),\n        \n        layers.Flatten(),\n        layers.Dense(512, activation='relu'),\n        layers.Dense(10, activation='softmax')    \n    ])","metadata":{"execution":{"iopub.status.busy":"2022-02-03T10:19:05.736238Z","iopub.execute_input":"2022-02-03T10:19:05.736926Z","iopub.status.idle":"2022-02-03T10:19:05.915224Z","shell.execute_reply.started":"2022-02-03T10:19:05.736893Z","shell.execute_reply":"2022-02-03T10:19:05.912169Z"}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model_cnn.compile(\n                loss = 'sparse_categorical_crossentropy',\n               optimizer = RMSprop(learning_rate=1e-4),\n               metrics = ['accuracy'])\n\n\nMODEL_2=model_cnn.fit(train_images, train_labels,\n                        epochs=20,\n                      verbose=1)","metadata":{"execution":{"iopub.status.busy":"2022-02-03T10:19:05.916677Z","iopub.execute_input":"2022-02-03T10:19:05.917166Z","iopub.status.idle":"2022-02-03T12:17:59.97502Z","shell.execute_reply.started":"2022-02-03T10:19:05.917125Z","shell.execute_reply":"2022-02-03T12:17:59.9726Z"}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"model_cnn.evaluate(train_images, train_labels)\n","metadata":{"execution":{"iopub.status.busy":"2022-02-03T12:17:59.98087Z","iopub.execute_input":"2022-02-03T12:17:59.981508Z","iopub.status.idle":"2022-02-03T12:19:56.815844Z","shell.execute_reply.started":"2022-02-03T12:17:59.98147Z","shell.execute_reply":"2022-02-03T12:19:56.814931Z"}}},{"cell_type":"markdown","source":"model_cnn.evaluate(test_images, test_labels)\n","metadata":{"execution":{"iopub.status.busy":"2022-02-03T12:19:56.819235Z","iopub.execute_input":"2022-02-03T12:19:56.819474Z","iopub.status.idle":"2022-02-03T12:20:20.717608Z","shell.execute_reply.started":"2022-02-03T12:19:56.819446Z","shell.execute_reply":"2022-02-03T12:20:20.716747Z"}}}]}