Task4-hand-gesture-recognization.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":61155,"sourceType":"datasetVersion","datasetId":39466}],"dockerImageVersionId":30698,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"\nimport os\nimport cv2\nimport numpy as np\nimport pandas as pd\nimport keras as keras\nimport tensorflow as tf\nfrom keras.optimizers import Adam\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.models import Sequential\nfrom sklearn.model_selection import train_test_split\nfrom tensorflow.keras.utils import load_img,img_to_array\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\nfrom tensorflow.keras.layers import Input, Conv2D, BatchNormalization, MaxPooling2D, Flatten, Dense, Dropout","metadata":{"execution":{"iopub.status.busy":"2024-05-15T03:34:11.342676Z","iopub.execute_input":"2024-05-15T03:34:11.343337Z","iopub.status.idle":"2024-05-15T03:34:11.351212Z","shell.execute_reply.started":"2024-05-15T03:34:11.343304Z","shell.execute_reply":"2024-05-15T03:34:11.350069Z"},"trusted":true},"execution_count":21,"outputs":[]},{"cell_type":"code","source":"dir = '/kaggle/input/leapgestrecog/leapGestRecog'","metadata":{"execution":{"iopub.status.busy":"2024-05-15T02:52:24.390003Z","iopub.execute_input":"2024-05-15T02:52:24.391109Z","iopub.status.idle":"2024-05-15T02:52:24.395379Z","shell.execute_reply.started":"2024-05-15T02:52:24.391069Z","shell.execute_reply":"2024-05-15T02:52:24.394447Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"images = []\nlabels = []\n\nfor directory in os.listdir(dir):\n    for subdir in os.listdir(os.path.join(dir,directory)):\n        for img in os.listdir(os.path.join(dir,directory,subdir)):\n            img_path = os.path.join(dir, directory, subdir, img)\n            images.append(img_path)\n            labels.append(subdir)","metadata":{"execution":{"iopub.status.busy":"2024-05-15T02:52:24.396567Z","iopub.execute_input":"2024-05-15T02:52:24.396820Z","iopub.status.idle":"2024-05-15T02:52:32.567334Z","shell.execute_reply.started":"2024-05-15T02:52:24.396798Z","shell.execute_reply":"2024-05-15T02:52:32.566304Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"img_series = pd.Series(images, name='Images')\nlab_series = pd.Series(labels, name='labels')\n\ndata = pd.concat([img_series, lab_series], axis = 1)\ndf = pd.DataFrame(data)","metadata":{"execution":{"iopub.status.busy":"2024-05-15T02:52:32.572665Z","iopub.execute_input":"2024-05-15T02:52:32.573293Z","iopub.status.idle":"2024-05-15T02:52:32.583700Z","shell.execute_reply.started":"2024-05-15T02:52:32.573266Z","shell.execute_reply":"2024-05-15T02:52:32.582812Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"df.head()","metadata":{"execution":{"iopub.status.busy":"2024-05-15T02:52:32.584798Z","iopub.execute_input":"2024-05-15T02:52:32.585162Z","iopub.status.idle":"2024-05-15T02:52:32.607477Z","shell.execute_reply.started":"2024-05-15T02:52:32.585129Z","shell.execute_reply":"2024-05-15T02:52:32.606330Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"                                              Images labels\n0  /kaggle/input/leapgestrecog/leapGestRecog/07/0...   02_l\n1  /kaggle/input/leapgestrecog/leapGestRecog/07/0...   02_l\n2  /kaggle/input/leapgestrecog/leapGestRecog/07/0...   02_l\n3  /kaggle/input/leapgestrecog/leapGestRecog/07/0...   02_l\n4  /kaggle/input/leapgestrecog/leapGestRecog/07/0...   02_l","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Images</th>\n      <th>labels</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>/kaggle/input/leapgestrecog/leapGestRecog/07/0...</td>\n      <td>02_l</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>/kaggle/input/leapgestrecog/leapGestRecog/07/0...</td>\n      <td>02_l</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>/kaggle/input/leapgestrecog/leapGestRecog/07/0...</td>\n      <td>02_l</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>/kaggle/input/leapgestrecog/leapGestRecog/07/0...</td>\n      <td>02_l</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>/kaggle/input/leapgestrecog/leapGestRecog/07/0...</td>\n      <td>02_l</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"image_shape = cv2.imread('/kaggle/input/leapgestrecog/leapGestRecog/05/07_ok/frame_05_07_0009.png')\nprint(image_shape.shape)","metadata":{"execution":{"iopub.status.busy":"2024-05-15T02:52:32.608567Z","iopub.execute_input":"2024-05-15T02:52:32.608885Z","iopub.status.idle":"2024-05-15T02:52:32.635147Z","shell.execute_reply.started":"2024-05-15T02:52:32.608827Z","shell.execute_reply":"2024-05-15T02:52:32.634266Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stdout","text":"(240, 640, 3)\n","output_type":"stream"}]},{"cell_type":"code","source":"pd.Series(labels).value_counts()","metadata":{"execution":{"iopub.status.busy":"2024-05-15T02:52:32.636294Z","iopub.execute_input":"2024-05-15T02:52:32.636611Z","iopub.status.idle":"2024-05-15T02:52:32.654257Z","shell.execute_reply.started":"2024-05-15T02:52:32.636581Z","shell.execute_reply":"2024-05-15T02:52:32.653394Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"02_l             2000\n04_fist_moved    2000\n09_c             2000\n10_down          2000\n06_index         2000\n08_palm_moved    2000\n07_ok            2000\n05_thumb         2000\n01_palm          2000\n03_fist          2000\nName: count, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"X_train, X_test = train_test_split(df, test_size=0.2, random_state=42)\ntrain_set, valid_set = train_test_split(df, test_size=0.3, random_state=42)","metadata":{"execution":{"iopub.status.busy":"2024-05-15T02:53:27.430378Z","iopub.execute_input":"2024-05-15T02:53:27.430725Z","iopub.status.idle":"2024-05-15T02:53:27.444931Z","shell.execute_reply.started":"2024-05-15T02:53:27.430698Z","shell.execute_reply":"2024-05-15T02:53:27.443900Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"code","source":"img_gen = ImageDataGenerator(preprocessing_function=tf.keras.applications.mobilenet_v2.preprocess_input)\n\ntrain = img_gen.flow_from_dataframe(dataframe= train_set,x_col=\"Images\",y_col=\"labels\",\n                                      target_size=(244,244),\n                                      color_mode='rgb',\n                                      class_mode=\"categorical\",\n                                      batch_size=4,\n                                      shuffle=False\n                                     )\n\ntest = img_gen.flow_from_dataframe(dataframe= X_test,x_col=\"Images\", y_col=\"labels\",\n                                     target_size=(244,244),\n                                     color_mode='rgb',\n                                     class_mode=\"categorical\",\n                                     batch_size=4,\n                                     shuffle= False\n                                    )\nvalid = img_gen.flow_from_dataframe(dataframe= valid_set,x_col=\"Images\", y_col=\"labels\",\n                                    target_size=(244,244),\n                                    color_mode= 'rgb',\n                                    class_mode=\"categorical\",\n                                    batch_size=4,\n                                    shuffle=False\n                                   )","metadata":{"execution":{"iopub.status.busy":"2024-05-15T02:53:33.679109Z","iopub.execute_input":"2024-05-15T02:53:33.679749Z","iopub.status.idle":"2024-05-15T02:53:42.577396Z","shell.execute_reply.started":"2024-05-15T02:53:33.679719Z","shell.execute_reply":"2024-05-15T02:53:42.576360Z"},"trusted":true},"execution_count":11,"outputs":[{"name":"stdout","text":"Found 14000 validated image filenames belonging to 10 classes.\nFound 4000 validated image filenames belonging to 10 classes.\nFound 6000 validated image filenames belonging to 10 classes.\n","output_type":"stream"}]},{"cell_type":"code","source":"classes = list(train.class_indices.keys())\nclasses","metadata":{"execution":{"iopub.status.busy":"2024-05-15T02:53:55.924264Z","iopub.execute_input":"2024-05-15T02:53:55.924880Z","iopub.status.idle":"2024-05-15T02:53:55.931375Z","shell.execute_reply.started":"2024-05-15T02:53:55.924836Z","shell.execute_reply":"2024-05-15T02:53:55.930320Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"['01_palm',\n '02_l',\n '03_fist',\n '04_fist_moved',\n '05_thumb',\n '06_index',\n '07_ok',\n '08_palm_moved',\n '09_c',\n '10_down']"},"metadata":{}}]},{"cell_type":"code","source":"input_shape = (244, 244, 3)\nnum_classes = 10","metadata":{"execution":{"iopub.status.busy":"2024-05-15T02:53:58.354150Z","iopub.execute_input":"2024-05-15T02:53:58.354870Z","iopub.status.idle":"2024-05-15T02:53:58.359216Z","shell.execute_reply.started":"2024-05-15T02:53:58.354828Z","shell.execute_reply":"2024-05-15T02:53:58.358259Z"},"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"code","source":"inputs = Input(shape=input_shape)\n\nx = Conv2D(filters=128, kernel_size=(8, 8), strides=(3, 3), activation='relu')(inputs)\nx = BatchNormalization()(x)\n\nx = Conv2D(filters=256, kernel_size=(5, 5), strides=(1, 1), activation='relu', padding=\"same\")(x)\nx = BatchNormalization()(x)\nx = MaxPooling2D(pool_size=(3, 3))(x)\n\nx = Conv2D(filters=256, kernel_size=(3, 3), strides=(1, 1), activation='relu', padding=\"same\")(x)\nx = BatchNormalization()(x)\nx = Conv2D(filters=256, kernel_size=(1, 1), strides=(1, 1), activation='relu', padding=\"same\")(x)\nx = BatchNormalization()(x)\nx = Conv2D(filters=256, kernel_size=(1, 1), strides=(1, 1), activation='relu', padding=\"same\")(x)\nx = BatchNormalization()(x)\n\nx = Conv2D(filters=512, kernel_size=(3, 3), activation='relu', padding=\"same\")(x)\nx = BatchNormalization()(x)\nx = MaxPooling2D(pool_size=(2, 2))(x)\n\nx = Conv2D(filters=512, kernel_size=(3, 3), activation='relu', padding=\"same\")(x)\nx = BatchNormalization()(x)\n\nx = Conv2D(filters=512, kernel_size=(3, 3), activation='relu', padding=\"same\")(x)\nx = BatchNormalization()(x)\n\nx = MaxPooling2D(pool_size=(2, 2))(x)\n\nx = Conv2D(filters=512, kernel_size=(3, 3), activation='relu', padding=\"same\")(x)\nx = BatchNormalization()(x)\n\nx = MaxPooling2D(pool_size=(2, 2))(x)\n\nx = Flatten()(x)\nx = Dense(1024, activation='relu')(x)\nx = Dropout(0.5)(x)\nx = Dense(1024, activation='relu')(x)\nx = Dropout(0.5)(x)\n\noutputs = Dense(num_classes, activation='softmax')(x)","metadata":{"execution":{"iopub.status.busy":"2024-05-15T02:54:19.409504Z","iopub.execute_input":"2024-05-15T02:54:19.409919Z","iopub.status.idle":"2024-05-15T02:54:20.286784Z","shell.execute_reply.started":"2024-05-15T02:54:19.409888Z","shell.execute_reply":"2024-05-15T02:54:20.285893Z"},"trusted":true},"execution_count":14,"outputs":[]},{"cell_type":"code","source":"model = Model(inputs=inputs, outputs=outputs)\n\nmodel.compile(\n    loss='categorical_crossentropy',\n    optimizer=tf.optimizers.SGD(learning_rate=0.001),\n    metrics=['accuracy']\n)\n\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2024-05-15T02:54:26.980047Z","iopub.execute_input":"2024-05-15T02:54:26.980739Z","iopub.status.idle":"2024-05-15T02:54:27.046310Z","shell.execute_reply.started":"2024-05-15T02:54:26.980708Z","shell.execute_reply":"2024-05-15T02:54:27.045266Z"},"trusted":true},"execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/plain":"\u001b[1mModel: \"functional_1\"\u001b[0m\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_1\"</span>\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n│ input_layer (\u001b[38;5;33mInputLayer\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m244\u001b[0m, \u001b[38;5;34m244\u001b[0m, \u001b[38;5;34m3\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m79\u001b[0m, \u001b[38;5;34m79\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │        \u001b[38;5;34m24,704\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m79\u001b[0m, \u001b[38;5;34m79\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │           \u001b[38;5;34m512\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m79\u001b[0m, \u001b[38;5;34m79\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │       \u001b[38;5;34m819,456\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_1           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m79\u001b[0m, \u001b[38;5;34m79\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │         \u001b[38;5;34m1,024\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │       \u001b[38;5;34m590,080\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_2           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │         \u001b[38;5;34m1,024\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │        \u001b[38;5;34m65,792\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_3           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │         \u001b[38;5;34m1,024\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_4 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │        \u001b[38;5;34m65,792\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_4           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │         \u001b[38;5;34m1,024\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_5 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │     \u001b[38;5;34m1,180,160\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_5           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │         \u001b[38;5;34m2,048\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_6 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │     \u001b[38;5;34m2,359,808\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_6           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │         \u001b[38;5;34m2,048\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_7 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │     \u001b[38;5;34m2,359,808\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_7           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │         \u001b[38;5;34m2,048\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_8 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │     \u001b[38;5;34m2,359,808\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_8           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │         \u001b[38;5;34m2,048\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_3 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4608\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)           │     \u001b[38;5;34m4,719,616\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dropout (\u001b[38;5;33mDropout\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)           │     \u001b[38;5;34m1,049,600\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │        \u001b[38;5;34m10,250\u001b[0m │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n│ input_layer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">244</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">244</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">79</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">79</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">24,704</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">79</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">79</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │           <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">79</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">79</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">819,456</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_1           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">79</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">79</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_2           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">65,792</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_3           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">65,792</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_4           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">1,180,160</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_5           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_6           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_7           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_8           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4608</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)           │     <span style=\"color: #00af00; text-decoration-color: #00af00\">4,719,616</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)           │     <span style=\"color: #00af00; text-decoration-color: #00af00\">1,049,600</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">10,250</span> │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m15,617,674\u001b[0m (59.58 MB)\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">15,617,674</span> (59.58 MB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m15,611,274\u001b[0m (59.55 MB)\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">15,611,274</span> (59.55 MB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m6,400\u001b[0m (25.00 KB)\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">6,400</span> (25.00 KB)\n</pre>\n"},"metadata":{}}]},{"cell_type":"code","source":"history = model.fit(train, epochs=3, validation_data=valid, verbose=1)","metadata":{"execution":{"iopub.status.busy":"2024-05-15T02:54:33.379026Z","iopub.execute_input":"2024-05-15T02:54:33.379758Z","iopub.status.idle":"2024-05-15T03:02:17.382911Z","shell.execute_reply.started":"2024-05-15T02:54:33.379730Z","shell.execute_reply":"2024-05-15T03:02:17.381892Z"},"trusted":true},"execution_count":16,"outputs":[{"name":"stdout","text":"Epoch 1/3\n","output_type":"stream"},{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:120: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n  self._warn_if_super_not_called()\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m   3/3500\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2:26\u001b[0m 42ms/step - accuracy: 0.1806 - loss: 3.6045   ","output_type":"stream"},{"name":"stderr","text":"WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nI0000 00:00:1715741688.610981      81 device_compiler.h:186] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\nW0000 00:00:1715741688.640054      81 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m3499/3500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - accuracy: 0.6143 - loss: 1.3133","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1715741836.082376      84 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m3500/3500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m225s\u001b[0m 60ms/step - accuracy: 0.6145 - loss: 1.3128 - val_accuracy: 0.9983 - val_loss: 0.0055\nEpoch 2/3\n\u001b[1m3500/3500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m119s\u001b[0m 34ms/step - accuracy: 0.9928 - loss: 0.0304 - val_accuracy: 0.9998 - val_loss: 0.0012\nEpoch 3/3\n\u001b[1m3500/3500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m120s\u001b[0m 34ms/step - accuracy: 0.9978 - loss: 0.0099 - val_accuracy: 0.9995 - val_loss: 0.0011\n","output_type":"stream"}]},{"cell_type":"code","source":"model.evaluate(test, verbose=1)","metadata":{"execution":{"iopub.status.busy":"2024-05-15T03:16:05.950442Z","iopub.execute_input":"2024-05-15T03:16:05.951102Z","iopub.status.idle":"2024-05-15T03:16:23.046675Z","shell.execute_reply.started":"2024-05-15T03:16:05.951068Z","shell.execute_reply":"2024-05-15T03:16:23.045664Z"},"trusted":true},"execution_count":17,"outputs":[{"name":"stdout","text":"\u001b[1m1000/1000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 17ms/step - accuracy: 0.9994 - loss: 0.0014\n","output_type":"stream"},{"execution_count":17,"output_type":"execute_result","data":{"text/plain":"[0.001258369185961783, 0.9994999766349792]"},"metadata":{}}]},{"cell_type":"code","source":"model.save('model.h5')","metadata":{"execution":{"iopub.status.busy":"2024-05-15T03:16:34.264493Z","iopub.execute_input":"2024-05-15T03:16:34.264891Z","iopub.status.idle":"2024-05-15T03:16:34.438224Z","shell.execute_reply.started":"2024-05-15T03:16:34.264861Z","shell.execute_reply":"2024-05-15T03:16:34.437236Z"},"trusted":true},"execution_count":18,"outputs":[]},{"cell_type":"code","source":"pred = model.predict(test)\npred = np.argmax(pred, axis=1)\n\nlabels = (train.class_indices)\nlabels = dict((v,k) for k,v in labels.items())\npred2 = [labels[k] for k in pred]","metadata":{"execution":{"iopub.status.busy":"2024-05-15T03:16:37.587328Z","iopub.execute_input":"2024-05-15T03:16:37.587726Z","iopub.status.idle":"2024-05-15T03:16:55.078477Z","shell.execute_reply.started":"2024-05-15T03:16:37.587695Z","shell.execute_reply":"2024-05-15T03:16:55.077385Z"},"trusted":true},"execution_count":19,"outputs":[{"name":"stdout","text":"\u001b[1m  13/1000\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m15s\u001b[0m 16ms/step","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1715742998.583958      84 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m1000/1000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 16ms/step\n","output_type":"stream"}]},{"cell_type":"code","source":"y_test = X_test.labels\nprint(classification_report(y_test, pred2))\nprint(\"Accuracy of the Model:\",\"{:.1f}%\".format(accuracy_score(y_test, pred2)*100))","metadata":{"execution":{"iopub.status.busy":"2024-05-15T03:16:59.457388Z","iopub.execute_input":"2024-05-15T03:16:59.457752Z","iopub.status.idle":"2024-05-15T03:16:59.629686Z","shell.execute_reply.started":"2024-05-15T03:16:59.457723Z","shell.execute_reply":"2024-05-15T03:16:59.628601Z"},"trusted":true},"execution_count":20,"outputs":[{"name":"stdout","text":"               precision    recall  f1-score   support\n\n      01_palm       1.00      1.00      1.00       410\n         02_l       1.00      1.00      1.00       417\n      03_fist       0.99      1.00      1.00       366\n04_fist_moved       1.00      1.00      1.00       404\n     05_thumb       1.00      1.00      1.00       409\n     06_index       1.00      1.00      1.00       377\n        07_ok       1.00      1.00      1.00       403\n08_palm_moved       1.00      1.00      1.00       392\n         09_c       1.00      1.00      1.00       404\n      10_down       1.00      1.00      1.00       418\n\n     accuracy                           1.00      4000\n    macro avg       1.00      1.00      1.00      4000\n weighted avg       1.00      1.00      1.00      4000\n\nAccuracy of the Model: 100.0%\n","output_type":"stream"}]},{"cell_type":"code","source":"img1 = load_img(\"/kaggle/input/leapgestrecog/leapGestRecog/05/05_thumb/frame_05_05_0027.png\",target_size=(244,244,3))\nimg1","metadata":{"execution":{"iopub.status.busy":"2024-05-15T04:14:19.472229Z","iopub.execute_input":"2024-05-15T04:14:19.472975Z","iopub.status.idle":"2024-05-15T04:14:19.504097Z","shell.execute_reply.started":"2024-05-15T04:14:19.472946Z","shell.execute_reply":"2024-05-15T04:14:19.503102Z"},"trusted":true},"execution_count":35,"outputs":[{"execution_count":35,"output_type":"execute_result","data":{"text/plain":"<PIL.Image.Image image mode=RGB size=244x244>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"img2 = load_img(\"/kaggle/input/leapgestrecog/leapGestRecog/05/01_palm/frame_05_01_0012.png\",target_size=(244,244,3))\nimg2","metadata":{"execution":{"iopub.status.busy":"2024-05-15T04:13:39.077520Z","iopub.execute_input":"2024-05-15T04:13:39.078212Z","iopub.status.idle":"2024-05-15T04:13:39.111245Z","shell.execute_reply.started":"2024-05-15T04:13:39.078179Z","shell.execute_reply":"2024-05-15T04:13:39.110172Z"},"trusted":true},"execution_count":34,"outputs":[{"execution_count":34,"output_type":"execute_result","data":{"text/plain":"<PIL.Image.Image image mode=RGB 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"},"metadata":{}}]},{"cell_type":"code","source":"img3 = load_img(\"/kaggle/input/leapgestrecog/leapGestRecog/05/03_fist/frame_05_03_0012.png\",target_size=(244,244,3))\nimg3","metadata":{"execution":{"iopub.status.busy":"2024-05-15T03:41:28.689579Z","iopub.execute_input":"2024-05-15T03:41:28.690322Z","iopub.status.idle":"2024-05-15T03:41:28.719868Z","shell.execute_reply.started":"2024-05-15T03:41:28.690289Z","shell.execute_reply":"2024-05-15T03:41:28.718972Z"},"trusted":true},"execution_count":30,"outputs":[{"execution_count":30,"output_type":"execute_result","data":{"text/plain":"<PIL.Image.Image image mode=RGB 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"},"metadata":{}}]},{"cell_type":"code","source":"img1 = img_to_array(img1)\nimg1 = img1 / 255.0 \nimg1 = np.expand_dims(img1, axis=0)\n\n\nimg2 = img_to_array(img2)\nimg2 = img2 / 255.0 \nimg2 = np.expand_dims(img2, axis=0) \n\n\nimg3 = img_to_array(img3)\nimg3 = img3 / 255.0 \nimg3 = np.expand_dims(img3, axis=0) \n\np1 = model.predict(img1).argmax()\np2 = model.predict(img2).argmax()\np3 = model.predict(img3).argmax()\n\nprint(\"Class for Image 1:\", p1)\nprint(\"Class for Image 2:\", p2)\nprint(\"Class for Image 3:\", p3)","metadata":{"execution":{"iopub.status.busy":"2024-05-15T04:14:24.471586Z","iopub.execute_input":"2024-05-15T04:14:24.472299Z","iopub.status.idle":"2024-05-15T04:14:24.700991Z","shell.execute_reply.started":"2024-05-15T04:14:24.472267Z","shell.execute_reply":"2024-05-15T04:14:24.700052Z"},"trusted":true},"execution_count":36,"outputs":[{"name":"stdout","text":"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\nClass for Image 1: 1\nClass for Image 2: 1\nClass for Image 3: 5\n","output_type":"stream"}]}]}