basic-concept-neural-network.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.8.16","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"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","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pip install tensorflow","metadata":{"execution":{"iopub.status.busy":"2023-02-21T16:45:01.575783Z","iopub.execute_input":"2023-02-21T16:45:01.576693Z","iopub.status.idle":"2023-02-21T16:45:05.318412Z","shell.execute_reply.started":"2023-02-21T16:45:01.576655Z","shell.execute_reply":"2023-02-21T16:45:05.317357Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"Requirement already satisfied: tensorflow in /usr/local/lib/python3.8/site-packages (2.11.0)\nRequirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.8/site-packages (from tensorflow) (15.0.6.1)\nRequirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.8/site-packages (from tensorflow) (1.22.4)\nRequirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.8/site-packages (from tensorflow) (0.2.0)\nRequirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.8/site-packages (from tensorflow) (1.51.1)\nRequirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.8/site-packages (from tensorflow) (1.6.3)\nRequirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.8/site-packages (from tensorflow) (2.2.0)\nRequirement already satisfied: tensorboard<2.12,>=2.11 in /usr/local/lib/python3.8/site-packages (from tensorflow) (2.11.2)\nRequirement already satisfied: packaging in /usr/local/lib/python3.8/site-packages (from tensorflow) (23.0)\nRequirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.8/site-packages (from tensorflow) (1.16.0)\nRequirement already satisfied: flatbuffers>=2.0 in /usr/local/lib/python3.8/site-packages (from tensorflow) (2.0.7)\nRequirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.8/site-packages (from tensorflow) (4.4.0)\nRequirement already satisfied: gast<=0.4.0,>=0.2.1 in /usr/local/lib/python3.8/site-packages (from tensorflow) (0.4.0)\nRequirement already satisfied: keras<2.12,>=2.11.0 in /usr/local/lib/python3.8/site-packages (from tensorflow) (2.11.0)\nRequirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.8/site-packages (from tensorflow) (0.30.0)\nRequirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.8/site-packages (from tensorflow) (3.3.0)\nRequirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.8/site-packages (from tensorflow) (1.14.1)\nRequirement already satisfied: setuptools in /usr/local/lib/python3.8/site-packages (from tensorflow) (57.5.0)\nRequirement already satisfied: protobuf<3.20,>=3.9.2 in /usr/local/lib/python3.8/site-packages (from tensorflow) (3.19.6)\nRequirement already satisfied: tensorflow-estimator<2.12,>=2.11.0 in /usr/local/lib/python3.8/site-packages (from tensorflow) (2.11.0)\nRequirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.8/site-packages (from tensorflow) (1.4.0)\nRequirement already satisfied: h5py>=2.9.0 in /usr/local/lib/python3.8/site-packages (from tensorflow) (3.8.0)\nRequirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.8/site-packages (from astunparse>=1.6.0->tensorflow) (0.38.4)\nRequirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.8/site-packages (from tensorboard<2.12,>=2.11->tensorflow) (3.4.1)\nRequirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.8/site-packages (from tensorboard<2.12,>=2.11->tensorflow) (0.6.1)\nRequirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.8/site-packages (from tensorboard<2.12,>=2.11->tensorflow) (2.16.0)\nRequirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.8/site-packages (from tensorboard<2.12,>=2.11->tensorflow) (0.4.6)\nRequirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.8/site-packages (from tensorboard<2.12,>=2.11->tensorflow) (2.2.2)\nRequirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.8/site-packages (from tensorboard<2.12,>=2.11->tensorflow) (2.28.2)\nRequirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.8/site-packages (from tensorboard<2.12,>=2.11->tensorflow) (1.8.1)\nRequirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.8/site-packages (from google-auth<3,>=1.6.3->tensorboard<2.12,>=2.11->tensorflow) (0.2.8)\nRequirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.8/site-packages (from google-auth<3,>=1.6.3->tensorboard<2.12,>=2.11->tensorflow) (5.3.0)\nRequirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.8/site-packages (from google-auth<3,>=1.6.3->tensorboard<2.12,>=2.11->tensorflow) (4.9)\nRequirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.8/site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.12,>=2.11->tensorflow) (1.3.1)\nRequirement already satisfied: importlib-metadata>=4.4 in /usr/local/lib/python3.8/site-packages (from markdown>=2.6.8->tensorboard<2.12,>=2.11->tensorflow) (6.0.0)\nRequirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/site-packages (from requests<3,>=2.21.0->tensorboard<2.12,>=2.11->tensorflow) (2021.10.8)\nRequirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.8/site-packages (from requests<3,>=2.21.0->tensorboard<2.12,>=2.11->tensorflow) (3.0.1)\nRequirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.8/site-packages (from requests<3,>=2.21.0->tensorboard<2.12,>=2.11->tensorflow) (3.4)\nRequirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/site-packages (from requests<3,>=2.21.0->tensorboard<2.12,>=2.11->tensorflow) (1.26.14)\nRequirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.8/site-packages (from werkzeug>=1.0.1->tensorboard<2.12,>=2.11->tensorflow) (2.1.2)\nRequirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.8/site-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard<2.12,>=2.11->tensorflow) (3.13.0)\nRequirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.8/site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.12,>=2.11->tensorflow) (0.4.8)\nRequirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.8/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.12,>=2.11->tensorflow) (3.2.2)\n\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n\u001b[0m\u001b[33mWARNING: You are using pip version 22.0.4; however, version 23.0.1 is available.\nYou should consider upgrading via the '/usr/local/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n\u001b[0mNote: you may need to restart the kernel to use updated packages.\n","output_type":"stream"}]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nfrom tensorflow.keras.models import Sequential, Model\nfrom tensorflow.keras.layers import Dense, Conv2D, Flatten, MaxPooling2D, Activation, Input, GlobalAveragePooling2D\nfrom tensorflow.keras.datasets import cifar10\nfrom tensorflow.keras.preprocessing import image\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n%matplotlib inline","metadata":{"execution":{"iopub.status.busy":"2023-02-21T16:45:35.412881Z","iopub.execute_input":"2023-02-21T16:45:35.413281Z","iopub.status.idle":"2023-02-21T16:45:35.421093Z","shell.execute_reply.started":"2023-02-21T16:45:35.413248Z","shell.execute_reply":"2023-02-21T16:45:35.420275Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"#Datasets splitting \n# x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, random_state = 42)\n\n(x_train, y_train), _ = cifar10.load_data()","metadata":{"execution":{"iopub.status.busy":"2023-02-21T16:46:03.623181Z","iopub.execute_input":"2023-02-21T16:46:03.624314Z","iopub.status.idle":"2023-02-21T16:46:08.277315Z","shell.execute_reply.started":"2023-02-21T16:46:03.624268Z","shell.execute_reply":"2023-02-21T16:46:08.276163Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stdout","text":"Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n170498071/170498071 [==============================] - 2s 0us/step\n","output_type":"stream"}]},{"cell_type":"code","source":"x_train","metadata":{"execution":{"iopub.status.busy":"2023-02-21T16:47:33.756339Z","iopub.execute_input":"2023-02-21T16:47:33.757351Z","iopub.status.idle":"2023-02-21T16:47:33.771176Z","shell.execute_reply.started":"2023-02-21T16:47:33.757305Z","shell.execute_reply":"2023-02-21T16:47:33.770239Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"array([[[[ 59,  62,  63],\n         [ 43,  46,  45],\n         [ 50,  48,  43],\n         ...,\n         [158, 132, 108],\n         [152, 125, 102],\n         [148, 124, 103]],\n\n        [[ 16,  20,  20],\n         [  0,   0,   0],\n         [ 18,   8,   0],\n         ...,\n         [123,  88,  55],\n         [119,  83,  50],\n         [122,  87,  57]],\n\n        [[ 25,  24,  21],\n         [ 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\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-02-21T16:59:42.870838Z","iopub.execute_input":"2023-02-21T16:59:42.871637Z","iopub.status.idle":"2023-02-21T16:59:43.902484Z","shell.execute_reply.started":"2023-02-21T16:59:42.871597Z","shell.execute_reply":"2023-02-21T16:59:43.901477Z"},"trusted":true},"execution_count":9,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 800x600 with 9 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"# Draw Neural Network","metadata":{}},{"cell_type":"code","source":"#the layer we will get , all the layer will be arrange sequentially by using Sequential()\nmodel = Sequential()\nmodel.add(Conv2D(input_shape = (32, 32, 3),\n                 filters =10, #we use it to remove all the noise from picture and here 10 means filtering by 10x10 matcrice\n                 kernel_size = (3, 3), #this is a dimension\n                 \n                 strides = (1, 1), #the step that we will jump or ignore. Here we will jump 1 by 1, ignore last row and colums only\n                 #strides = (2, 2),\n                 padding = \"valid\" #adding extra padding into main matrice. By default it is valid\n                ))\n\nmodel.output_shape","metadata":{"execution":{"iopub.status.busy":"2023-02-21T17:15:56.208949Z","iopub.execute_input":"2023-02-21T17:15:56.209860Z","iopub.status.idle":"2023-02-21T17:15:56.283312Z","shell.execute_reply.started":"2023-02-21T17:15:56.209807Z","shell.execute_reply":"2023-02-21T17:15:56.282389Z"},"trusted":true},"execution_count":13,"outputs":[{"name":"stderr","text":"2023-02-21 17:15:56.218490: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n2023-02-21 17:15:56.218552: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: UNKNOWN ERROR (303)\n2023-02-21 17:15:56.218598: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (0c960c34593a): /proc/driver/nvidia/version does not exist\n2023-02-21 17:15:56.218963: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n","output_type":"stream"},{"execution_count":13,"output_type":"execute_result","data":{"text/plain":"(None, 30, 30, 10)"},"metadata":{}}]},{"cell_type":"code","source":"#the layer we will get , all the layer will be arrange sequentially by using Sequential()\nmodel = Sequential()\nmodel.add(Conv2D(input_shape = (32, 32, 3),\n                 filters =10, #we use it to remove all the noise from picture and here 10 means filtering by 10x10 matcrice\n                 kernel_size = (3, 3), #this is a dimension\n                 \n                 strides = (1, 1), #the step that we will jump or ignore. Here we will jump 1 by 1, ignore last row and colums only\n                 #strides = (2, 2),\n                 padding = \"same\" \n                ))\n\nmodel.output_shape","metadata":{"execution":{"iopub.status.busy":"2023-02-21T17:23:01.547583Z","iopub.execute_input":"2023-02-21T17:23:01.548468Z","iopub.status.idle":"2023-02-21T17:23:01.571694Z","shell.execute_reply.started":"2023-02-21T17:23:01.548430Z","shell.execute_reply":"2023-02-21T17:23:01.570833Z"},"trusted":true},"execution_count":14,"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"(None, 32, 32, 10)"},"metadata":{}}]},{"cell_type":"code","source":"#the layer we will get , all the layer will be arrange sequentially by using Sequential()\nmodel = Sequential()\nmodel.add(Conv2D(input_shape = (32, 32, 3),\n                 filters =10, #we use it to remove all the noise from picture and here 10 means filtering by 10x10 matcrice\n                 kernel_size = (3, 3), #this is a dimension\n                 \n                 strides = (1, 1), #the step that we will jump or ignore. Here we will jump 1 by 1, ignore last row and colums only\n                 #strides = (2, 2),\n                 padding = \"valid\" #adding extra padding into main matrice. By default it is valid\n                ))\n\nmodel.add(MaxPooling2D(pool_size = (2, 2)))\nmodel.output_shape","metadata":{"execution":{"iopub.status.busy":"2023-02-21T17:40:17.319502Z","iopub.execute_input":"2023-02-21T17:40:17.320626Z","iopub.status.idle":"2023-02-21T17:40:17.348389Z","shell.execute_reply.started":"2023-02-21T17:40:17.320566Z","shell.execute_reply":"2023-02-21T17:40:17.347595Z"},"trusted":true},"execution_count":15,"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":"(None, 15, 15, 10)"},"metadata":{}}]},{"cell_type":"code","source":"#the layer we will get , all the layer will be arrange sequentially by using Sequential()\nmodel = Sequential()\nmodel.add(Conv2D(input_shape = (32, 32, 3),\n                 filters =10, #we use it to remove all the noise from picture and here 10 means filtering by 10x10 matcrice\n                 kernel_size = (3, 3), #this is a dimension\n                 strides = (1, 1), #the step that we will jump or ignore. Here we will jump 1 by 1, ignore last row and colums only\n                 padding = \"same\" \n                ))\n\nmodel.add(MaxPooling2D(pool_size = (2, 2)))\nmodel.output_shape","metadata":{"execution":{"iopub.status.busy":"2023-02-21T17:40:58.781489Z","iopub.execute_input":"2023-02-21T17:40:58.782327Z","iopub.status.idle":"2023-02-21T17:40:58.811634Z","shell.execute_reply.started":"2023-02-21T17:40:58.782288Z","shell.execute_reply":"2023-02-21T17:40:58.810764Z"},"trusted":true},"execution_count":16,"outputs":[{"execution_count":16,"output_type":"execute_result","data":{"text/plain":"(None, 16, 16, 10)"},"metadata":{}}]},{"cell_type":"code","source":"#the layer we will get , all the layer will be arrange sequentially by using Sequential()\nmodel = Sequential()\nmodel.add(Conv2D(input_shape = (32, 32, 3),\n                 filters =10, #we use it to remove all the noise from picture and here 10 means filtering by 10x10 matcrice\n                 kernel_size = (3, 3), #this is a dimension\n                 \n                 strides = (1, 1), #the step that we will jump or ignore. Here we will jump 1 by 1, ignore last row and colums only\n                 #strides = (2, 2),\n                 padding = \"valid\" \n                ))\n\nmodel.add(MaxPooling2D(pool_size = (2, 2), padding = \"valid\"))\nmodel.output_shape","metadata":{"execution":{"iopub.status.busy":"2023-02-21T17:45:17.207686Z","iopub.execute_input":"2023-02-21T17:45:17.208987Z","iopub.status.idle":"2023-02-21T17:45:17.238597Z","shell.execute_reply.started":"2023-02-21T17:45:17.208948Z","shell.execute_reply":"2023-02-21T17:45:17.237701Z"},"trusted":true},"execution_count":17,"outputs":[{"execution_count":17,"output_type":"execute_result","data":{"text/plain":"(None, 15, 15, 10)"},"metadata":{}}]},{"cell_type":"code","source":"#the layer we will get , all the layer will be arrange sequentially by using Sequential()\nmodel = Sequential()\nmodel.add(Conv2D(input_shape = (32, 32, 3),\n                 filters =10, #we use it to remove all the noise from picture and here 10 means filtering by 10x10 matcrice\n                 kernel_size = (3, 3), #this is a dimension\n                 \n                 strides = (1, 1), #the step that we will jump or ignore. Here we will jump 1 by 1, ignore last row and colums only\n                 #strides = (2, 2),\n                 padding = \"valid\" \n                ))\n\nmodel.add(MaxPooling2D(pool_size = (2, 2), padding = \"same\"))\nmodel.output_shape","metadata":{"execution":{"iopub.status.busy":"2023-02-21T17:46:04.177877Z","iopub.execute_input":"2023-02-21T17:46:04.178278Z","iopub.status.idle":"2023-02-21T17:46:04.208851Z","shell.execute_reply.started":"2023-02-21T17:46:04.178249Z","shell.execute_reply":"2023-02-21T17:46:04.207969Z"},"trusted":true},"execution_count":18,"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"(None, 15, 15, 10)"},"metadata":{}}]},{"cell_type":"code","source":"model  = Sequential()\nmodel.add(Conv2D(input_shape = (32, 32, 3),\n                 filters =10, \n                 kernel_size = (3, 3),\n                 strides = (1, 1),\n                 padding = \"valid\"\n                ))\nmodel.add(MaxPooling2D(pool_size  = (2, 2), padding = \"valid\", strides  =(1, 1)))\nmodel.output_shape","metadata":{"execution":{"iopub.status.busy":"2023-02-21T17:46:26.897281Z","iopub.execute_input":"2023-02-21T17:46:26.897691Z","iopub.status.idle":"2023-02-21T17:46:26.926016Z","shell.execute_reply.started":"2023-02-21T17:46:26.897659Z","shell.execute_reply":"2023-02-21T17:46:26.925177Z"},"trusted":true},"execution_count":19,"outputs":[{"execution_count":19,"output_type":"execute_result","data":{"text/plain":"(None, 29, 29, 10)"},"metadata":{}}]},{"cell_type":"code","source":"model  = Sequential()\nmodel.add(Conv2D(input_shape = (32, 32, 3),\n                 filters =10, \n                 kernel_size = (3, 3),\n                 strides = (1, 1),\n                 padding = \"valid\"\n                ))\nmodel.add(MaxPooling2D(pool_size  = (2, 2), padding = \"valid\", strides  =(2, 2)))\nmodel.output_shape","metadata":{"execution":{"iopub.status.busy":"2023-02-21T17:46:44.060098Z","iopub.execute_input":"2023-02-21T17:46:44.060489Z","iopub.status.idle":"2023-02-21T17:46:44.087507Z","shell.execute_reply.started":"2023-02-21T17:46:44.060457Z","shell.execute_reply":"2023-02-21T17:46:44.086708Z"},"trusted":true},"execution_count":20,"outputs":[{"execution_count":20,"output_type":"execute_result","data":{"text/plain":"(None, 15, 15, 10)"},"metadata":{}}]},{"cell_type":"code","source":"model  = Sequential()\nmodel.add(Conv2D(input_shape = (32, 32, 3),\n                 filters =10, \n                 kernel_size = (3, 3),\n                 strides = (1, 1),\n                 padding = \"valid\"\n                ))\nmodel.add(MaxPooling2D(pool_size  = (2, 2), padding = \"same\", strides  =(1, 1)))\nmodel.output_shape","metadata":{"execution":{"iopub.status.busy":"2023-02-21T17:47:50.524805Z","iopub.execute_input":"2023-02-21T17:47:50.525196Z","iopub.status.idle":"2023-02-21T17:47:50.552873Z","shell.execute_reply.started":"2023-02-21T17:47:50.525166Z","shell.execute_reply":"2023-02-21T17:47:50.552072Z"},"trusted":true},"execution_count":21,"outputs":[{"execution_count":21,"output_type":"execute_result","data":{"text/plain":"(None, 30, 30, 10)"},"metadata":{}}]},{"cell_type":"code","source":"model  = Sequential()\nmodel.add(Conv2D(input_shape = (32, 32, 3),\n                 filters =10, \n                 kernel_size = (3, 3),\n                 strides = (1, 1),\n                 padding = \"same\"\n                ))\nmodel.add(MaxPooling2D(pool_size  = (2, 2), padding = \"same\", strides  =(1, 1)))\nmodel.output_shape","metadata":{"execution":{"iopub.status.busy":"2023-02-21T17:48:07.869923Z","iopub.execute_input":"2023-02-21T17:48:07.870800Z","iopub.status.idle":"2023-02-21T17:48:07.897244Z","shell.execute_reply.started":"2023-02-21T17:48:07.870767Z","shell.execute_reply":"2023-02-21T17:48:07.896333Z"},"trusted":true},"execution_count":22,"outputs":[{"execution_count":22,"output_type":"execute_result","data":{"text/plain":"(None, 32, 32, 10)"},"metadata":{}}]},{"cell_type":"code","source":"model  = Sequential()\nmodel.add(Conv2D(input_shape = (32, 32, 3),\n                 filters =10, \n                 kernel_size = (3, 3),\n                 strides = (1, 1),\n                 padding = \"valid\"\n                ))\nmodel.add(GlobalAveragePooling2D()) #ignore all the properties except filtering value\nmodel.output_shape","metadata":{"execution":{"iopub.status.busy":"2023-02-21T17:52:20.044361Z","iopub.execute_input":"2023-02-21T17:52:20.045367Z","iopub.status.idle":"2023-02-21T17:52:20.073645Z","shell.execute_reply.started":"2023-02-21T17:52:20.045309Z","shell.execute_reply":"2023-02-21T17:52:20.072754Z"},"trusted":true},"execution_count":23,"outputs":[{"execution_count":23,"output_type":"execute_result","data":{"text/plain":"(None, 10)"},"metadata":{}}]},{"cell_type":"code","source":"model  = Sequential()\nmodel.add(Conv2D(input_shape = (32, 32, 3),\n                 filters =10, \n                 kernel_size = (3, 3),\n                 strides = (1, 1),\n                 padding = \"valid\"\n                ))\nprint(model.output_shape)\nmodel.add(Flatten()) #converting multi-dimensional data into single dimensional data(FCNN- Fully connected neural network)\nprint(model.output_shape)","metadata":{"execution":{"iopub.status.busy":"2023-02-21T17:52:48.946265Z","iopub.execute_input":"2023-02-21T17:52:48.947027Z","iopub.status.idle":"2023-02-21T17:52:48.975240Z","shell.execute_reply.started":"2023-02-21T17:52:48.946984Z","shell.execute_reply":"2023-02-21T17:52:48.974170Z"},"trusted":true},"execution_count":24,"outputs":[{"name":"stdout","text":"(None, 30, 30, 10)\n(None, 9000)\n","output_type":"stream"}]},{"cell_type":"code","source":"model  = Sequential()\nmodel.add(Conv2D(input_shape = (32, 32, 3),\n                 filters =10, \n                 kernel_size = (3, 3),\n                 strides = (1, 1),\n                 padding = \"same\"\n                ))\nprint(model.output_shape)\nmodel.add(Flatten())\nprint(model.output_shape)","metadata":{"execution":{"iopub.status.busy":"2023-02-21T17:53:23.995920Z","iopub.execute_input":"2023-02-21T17:53:23.996657Z","iopub.status.idle":"2023-02-21T17:53:24.021462Z","shell.execute_reply.started":"2023-02-21T17:53:23.996620Z","shell.execute_reply":"2023-02-21T17:53:24.020509Z"},"trusted":true},"execution_count":25,"outputs":[{"name":"stdout","text":"(None, 32, 32, 10)\n(None, 10240)\n","output_type":"stream"}]},{"cell_type":"code","source":"model  = Sequential()\nmodel.add(Conv2D(input_shape = (32, 32, 3),\n                 filters =10, \n                 kernel_size = (3, 3),\n                 strides = (2, 2),\n                 padding = \"same\"\n                ))\nprint(model.output_shape)\nmodel.add(Flatten())\nprint(model.output_shape)","metadata":{"execution":{"iopub.status.busy":"2023-02-21T17:53:49.689119Z","iopub.execute_input":"2023-02-21T17:53:49.690200Z","iopub.status.idle":"2023-02-21T17:53:49.716490Z","shell.execute_reply.started":"2023-02-21T17:53:49.690160Z","shell.execute_reply":"2023-02-21T17:53:49.715444Z"},"trusted":true},"execution_count":26,"outputs":[{"name":"stdout","text":"(None, 16, 16, 10)\n(None, 2560)\n","output_type":"stream"}]},{"cell_type":"code","source":"model  = Sequential()\nmodel.add(Conv2D(input_shape = (32, 32, 3),\n                 filters =10, \n                 kernel_size = (3, 3),\n                 strides = (2, 2),\n                 padding = \"valid\"\n                ))\nprint(model.output_shape)\nmodel.add(Flatten())\nprint(model.output_shape)","metadata":{"execution":{"iopub.status.busy":"2023-02-21T17:54:07.782342Z","iopub.execute_input":"2023-02-21T17:54:07.782973Z","iopub.status.idle":"2023-02-21T17:54:07.807727Z","shell.execute_reply.started":"2023-02-21T17:54:07.782935Z","shell.execute_reply":"2023-02-21T17:54:07.806668Z"},"trusted":true},"execution_count":27,"outputs":[{"name":"stdout","text":"(None, 15, 15, 10)\n(None, 2250)\n","output_type":"stream"}]},{"cell_type":"code","source":"model  = Sequential()\nmodel.add(Conv2D(input_shape = (32, 32, 3),\n                 filters =10, \n                 kernel_size = (3, 3),\n                 strides = (1, 1),\n                 padding = \"valid\"\n                ))\nprint(model.output_shape)\nmodel.add(Flatten())\nprint(model.output_shape)\nmodel.add(Dense(10))\nprint(model.output_shape)","metadata":{"execution":{"iopub.status.busy":"2023-02-21T17:59:40.794478Z","iopub.execute_input":"2023-02-21T17:59:40.795634Z","iopub.status.idle":"2023-02-21T17:59:40.835282Z","shell.execute_reply.started":"2023-02-21T17:59:40.795597Z","shell.execute_reply":"2023-02-21T17:59:40.834277Z"},"trusted":true},"execution_count":28,"outputs":[{"name":"stdout","text":"(None, 30, 30, 10)\n(None, 9000)\n(None, 10)\n","output_type":"stream"}]},{"cell_type":"code","source":"model  = Sequential()\nmodel.add(Conv2D(input_shape = (32, 32, 3),\n                 filters =10, \n                 kernel_size = (3, 3),\n                 strides = (1, 1),\n                 padding = \"valid\"\n                ))\nprint(model.output_shape)\nmodel.add(Flatten())\nprint(model.output_shape)\nmodel.add(Dense(2)) #FCNN or dense layer\nprint(model.output_shape)","metadata":{"execution":{"iopub.status.busy":"2023-02-21T17:59:49.911103Z","iopub.execute_input":"2023-02-21T17:59:49.912167Z","iopub.status.idle":"2023-02-21T17:59:49.948340Z","shell.execute_reply.started":"2023-02-21T17:59:49.912131Z","shell.execute_reply":"2023-02-21T17:59:49.947296Z"},"trusted":true},"execution_count":29,"outputs":[{"name":"stdout","text":"(None, 30, 30, 10)\n(None, 9000)\n(None, 2)\n","output_type":"stream"}]}]}