Deep Learning Projects/neural-nets-multiclass-classification.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","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":"import tensorflow as tf \nfrom tensorflow.keras.datasets import fashion_mnist\nfrom tensorflow import keras","metadata":{"execution":{"iopub.status.busy":"2023-07-24T10:13:48.571215Z","iopub.execute_input":"2023-07-24T10:13:48.571607Z","iopub.status.idle":"2023-07-24T10:13:48.578140Z","shell.execute_reply.started":"2023-07-24T10:13:48.571574Z","shell.execute_reply":"2023-07-24T10:13:48.577167Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"(train_data, train_labels), (test_data, test_labels) = fashion_mnist.load_data()","metadata":{"execution":{"iopub.status.busy":"2023-07-24T10:14:39.758934Z","iopub.execute_input":"2023-07-24T10:14:39.759692Z","iopub.status.idle":"2023-07-24T10:14:43.746928Z","shell.execute_reply.started":"2023-07-24T10:14:39.759652Z","shell.execute_reply":"2023-07-24T10:14:43.746028Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n29515/29515 [==============================] - 0s 1us/step\nDownloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n26421880/26421880 [==============================] - 2s 0us/step\nDownloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n5148/5148 [==============================] - 0s 0us/step\nDownloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n4422102/4422102 [==============================] - 1s 0us/step\n","output_type":"stream"}]},{"cell_type":"code","source":"train_data.shape, test_data.shape","metadata":{"execution":{"iopub.status.busy":"2023-07-24T10:15:09.216659Z","iopub.execute_input":"2023-07-24T10:15:09.217050Z","iopub.status.idle":"2023-07-24T10:15:09.225288Z","shell.execute_reply.started":"2023-07-24T10:15:09.217011Z","shell.execute_reply":"2023-07-24T10:15:09.224246Z"},"trusted":true},"execution_count":3,"outputs":[{"execution_count":3,"output_type":"execute_result","data":{"text/plain":"((60000, 28, 28), (10000, 28, 28))"},"metadata":{}}]},{"cell_type":"code","source":"train_data[0]","metadata":{"execution":{"iopub.status.busy":"2023-07-24T10:15:18.861377Z","iopub.execute_input":"2023-07-24T10:15:18.861771Z","iopub.status.idle":"2023-07-24T10:15:18.871451Z","shell.execute_reply.started":"2023-07-24T10:15:18.861742Z","shell.execute_reply":"2023-07-24T10:15:18.870341Z"},"trusted":true},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"array([[  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0],\n       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0],\n       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0],\n       [  0,   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[  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n        183, 225, 216, 223, 228, 235, 227, 224, 222, 224, 221, 223, 245,\n        173,   0],\n       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n        193, 228, 218, 213, 198, 180, 212, 210, 211, 213, 223, 220, 243,\n        202,   0],\n       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   1,   3,   0,  12,\n        219, 220, 212, 218, 192, 169, 227, 208, 218, 224, 212, 226, 197,\n        209,  52],\n       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   6,   0,  99,\n        244, 222, 220, 218, 203, 198, 221, 215, 213, 222, 220, 245, 119,\n        167,  56],\n       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   4,   0,   0,  55,\n        236, 228, 230, 228, 240, 232, 213, 218, 223, 234, 217, 217, 209,\n         92,   0],\n       [  0,   0,   1,   4,   6,   7,   2,   0,   0,   0,   0,   0, 237,\n        226, 217, 223, 222, 219, 222, 221, 216, 223, 229, 215, 218, 255,\n         77,   0],\n       [  0,   3,   0,   0,   0,   0,   0,   0,   0,  62, 145, 204, 228,\n        207, 213, 221, 218, 208, 211, 218, 224, 223, 219, 215, 224, 244,\n        159,   0],\n       [  0,   0,   0,   0,  18,  44,  82, 107, 189, 228, 220, 222, 217,\n        226, 200, 205, 211, 230, 224, 234, 176, 188, 250, 248, 233, 238,\n        215,   0],\n       [  0,  57, 187, 208, 224, 221, 224, 208, 204, 214, 208, 209, 200,\n        159, 245, 193, 206, 223, 255, 255, 221, 234, 221, 211, 220, 232,\n        246,   0],\n       [  3, 202, 228, 224, 221, 211, 211, 214, 205, 205, 205, 220, 240,\n         80, 150, 255, 229, 221, 188, 154, 191, 210, 204, 209, 222, 228,\n        225,   0],\n       [ 98, 233, 198, 210, 222, 229, 229, 234, 249, 220, 194, 215, 217,\n        241,  65,  73, 106, 117, 168, 219, 221, 215, 217, 223, 223, 224,\n        229,  29],\n       [ 75, 204, 212, 204, 193, 205, 211, 225, 216, 185, 197, 206, 198,\n        213, 240, 195, 227, 245, 239, 223, 218, 212, 209, 222, 220, 221,\n        230,  67],\n       [ 48, 203, 183, 194, 213, 197, 185, 190, 194, 192, 202, 214, 219,\n        221, 220, 236, 225, 216, 199, 206, 186, 181, 177, 172, 181, 205,\n        206, 115],\n       [  0, 122, 219, 193, 179, 171, 183, 196, 204, 210, 213, 207, 211,\n        210, 200, 196, 194, 191, 195, 191, 198, 192, 176, 156, 167, 177,\n        210,  92],\n       [  0,   0,  74, 189, 212, 191, 175, 172, 175, 181, 185, 188, 189,\n        188, 193, 198, 204, 209, 210, 210, 211, 188, 188, 194, 192, 216,\n        170,   0],\n       [  2,   0,   0,   0,  66, 200, 222, 237, 239, 242, 246, 243, 244,\n        221, 220, 193, 191, 179, 182, 182, 181, 176, 166, 168,  99,  58,\n          0,   0],\n       [  0,   0,   0,   0,   0,   0,   0,  40,  61,  44,  72,  41,  35,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0],\n       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0],\n       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0]], dtype=uint8)"},"metadata":{}}]},{"cell_type":"code","source":"train_labels[0]","metadata":{"execution":{"iopub.status.busy":"2023-07-24T10:15:37.566282Z","iopub.execute_input":"2023-07-24T10:15:37.567182Z","iopub.status.idle":"2023-07-24T10:15:37.574144Z","shell.execute_reply.started":"2023-07-24T10:15:37.567146Z","shell.execute_reply":"2023-07-24T10:15:37.572996Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"9"},"metadata":{}}]},{"cell_type":"code","source":"# plot one image\nimport matplotlib.pyplot as plt \nplt.imshow(train_data[10])","metadata":{"execution":{"iopub.status.busy":"2023-07-24T10:16:49.951559Z","iopub.execute_input":"2023-07-24T10:16:49.951947Z","iopub.status.idle":"2023-07-24T10:16:50.201522Z","shell.execute_reply.started":"2023-07-24T10:16:49.951915Z","shell.execute_reply":"2023-07-24T10:16:50.200473Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"<matplotlib.image.AxesImage at 0x7bcfbda88970>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAaEAAAGdCAYAAAC7EMwUAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAhMUlEQVR4nO3dbWxU993m8Ws8to+NGSbrgu1xMK6bG7ZpoORuoBCUB8g2Vrxb1IRUIonUhVttlDSAlnWiqJQXsaoVjtINywsaqkYVBTU0aKU87cKGuCU2jShdwpINpdmUFBOcBtfEAY/xw4zH898XvuP7NhDI72D774fvRxoJz8zF+fv4mGsOM/ObiHPOCQAAD3J8LwAAMHlRQgAAbyghAIA3lBAAwBtKCADgDSUEAPCGEgIAeEMJAQC8yfW9gItls1l9/PHHisViikQivpcDADByzqmzs1Pl5eXKybnyuc6YK6GPP/5YFRUVvpcBALhGLS0tmjlz5hXvM+ZKKBaLSZJu079XrvI8r2ZyiF43LVTu/f9ygzmz/Ov/15z57WsLzZnr/+sfzRlcm/Z/+qY5U7HypDnzfpP9uKuo53gYTRn16S3tHfz3/EpGrISee+45/fSnP9WZM2d00003acuWLbr99tuvmvvsv+BylafcCCU0GqKR/FC5nMICcyaYav+ZRgP7djh2Rl803/5zyiuyH3vRAo6HMe+fJ5J+kadURuSFCbt379b69eu1ceNGHT16VLfffrtqamp0+vTpkdgcAGCcGpES2rx5s77//e/rBz/4gW688UZt2bJFFRUV2rZt20hsDgAwTg17CaXTaR05ckTV1dVDrq+urtbBgwcvuX8qlVIymRxyAQBMDsNeQp988on6+/tVWlo65PrS0lK1trZecv/6+nrF4/HBC6+MA4DJY8TerHrxE1LOucs+SbVhwwZ1dHQMXlpaWkZqSQCAMWbYXx03ffp0RaPRS8562traLjk7kqQgCBQEwXAvAwAwDgz7mVB+fr5uueUWNTQ0DLm+oaFBS5YsGe7NAQDGsRF5n1Btba2+973vacGCBbr11lv1i1/8QqdPn9ajjz46EpsDAIxTI1JCK1euVHt7u37yk5/ozJkzmjt3rvbu3avKysqR2BwAYJyKOOec70X8a8lkUvF4XEv1Hd7lHMJfd91szvznm38XalsFkT5z5lDSPnJlTcl+c+Z/91aZM5L02/YbzZkjzbPMmWyn/djOvS5tzvzw6wfMGUmKR7vNmdnBpa9+vZrfdd5kzszKbzdnGj79mjkjSR0/LDFnsu/+v1Dbmkgyrk+NelUdHR2aNu3KY8H4KAcAgDeUEADAG0oIAOANJQQA8IYSAgB4QwkBALyhhAAA3lBCAABvKCEAgDeUEADAG0oIAOANJQQA8IYBpmNY1/2LzJmS/3TSnDl1vtickaSSqRfMmZyI/XArDuzDNL8x7bQ5I0nleefMmbeSc8yZvcfnmjPfnvuuOfOlvC5zRpL+2j3dnHmvvcyc+bfFbeZMc9J+vFbEzpszktTadeXhm5cTVJ8Kta2JhAGmAIBxgRICAHhDCQEAvKGEAADeUEIAAG8oIQCAN5QQAMAbSggA4A0lBADwhhICAHhDCQEAvKGEAADeUEIAAG9yfS8An+9v/84+cfrvH11vzuQHfeaMJPVm7FPOC3Lt2/rgvH2ic29/uEM7zJTv/Jx+c+abs5vNmU/TReZMa699CrQUbnr0N0pazJmzvVPNmWiIn9Gf/p4wZyRp+lT7FPLUf1hozgR7DpszEwVnQgAAbyghAIA3lBAAwBtKCADgDSUEAPCGEgIAeEMJAQC8oYQAAN5QQgAAbyghAIA3lBAAwBtKCADgDQNMx7CiMvvwxO7OwL6hEBFJ6s3YD5+8qH3YZ1F+2py50Bfum2rvtg8JDXIz5kyYQal9WftjxkRR0pyRpOKCbnMmzDDSv3fHzJmsi5gz0ZysORN2W623238vqvaYIxMGZ0IAAG8oIQCAN5QQAMAbSggA4A0lBADwhhICAHhDCQEAvKGEAADeUEIAAG8oIQCAN5QQAMAbSggA4A0DTEdLTtQcmT7VPsD0dLLAnOkOkZGkKUFfqJxVELUPCC2IhlzbFHukIMT6ujL55kyh7ENPc0MO7iyIpsyZvIh9W1Ny7T+nT1Mhfkgh9YcZlnrDhRFYycTFmRAAwBtKCADgzbCXUF1dnSKRyJBLWVnZcG8GADABjMhzQjfddJN++9vfDn4djdqfDwEATHwjUkK5ubmc/QAArmpEnhM6ceKEysvLVVVVpQceeEAnT5783PumUiklk8khFwDA5DDsJbRo0SLt3LlT+/bt0/PPP6/W1lYtWbJE7e3tl71/fX294vH44KWiomK4lwQAGKOGvYRqamp0//33a968efrWt76lPXv2SJJ27Nhx2ftv2LBBHR0dg5eWlpbhXhIAYIwa8TerFhUVad68eTpx4sRlbw+CQEEQjPQyAABj0Ii/TyiVSum9995TIpEY6U0BAMaZYS+hJ554Qk1NTWpubtYf//hHffe731UymdSqVauGe1MAgHFu2P877qOPPtKDDz6oTz75RDNmzNDixYt16NAhVVZWDvemAADj3LCX0Isvvjjcf+WEkDNvjjkTzbEPMM0tsA+E7EuGe07uXEeROZOfax/2eUO8w5zp7c8zZyRpap59cGdOJMxg0f5R2U53iEGpUrgBsGHWl3H2/4zJhhgq2tkTbkhvGDeWtpoz9t/0iYPZcQAAbyghAIA3lBAAwBtKCADgDSUEAPCGEgIAeEMJAQC8oYQAAN5QQgAAbyghAIA3lBAAwBtKCADgzYh/qB0G9Mycas70pu1DJF02xOMK+zxISVJOi30o5NmcrDlzvqvQnImE/J7iU3rMmXTG/mvUn7UvMMx28qL2QamSdC6w7/P+EMdeT9o+aDb5d/vvUs4U++BcSZoy1T7Q9tT5YnMmUWEfIpxp+cicGYs4EwIAeEMJAQC8oYQAAN5QQgAAbyghAIA3lBAAwBtKCADgDSUEAPCGEgIAeEMJAQC8oYQAAN5QQgAAbyghAIA3TNEeJd0z7Lv67N/j5syUab3mzPqbf2fOSNKW//ltcybbap/O7Ert31N+YJ9ALkkXeu3TjNN99p+tc+aIsv32x4zpSNS+IUlBnn3qdCrEfkietU/Erv7HP5kzmWy4/dB08h/Mmbyp9knsF24uN2cKmKINAMC1oYQAAN5QQgAAbyghAIA3lBAAwBtKCADgDSUEAPCGEgIAeEMJAQC8oYQAAN5QQgAAbyghAIA3DDAdJT0zIuZMUJQ2Z+q//rI5szBoM2ck6b/ffIs50/oH+6DGkq91mDNnk/bBmJKUztofl+XkZM2Zvj77QM28fPtQ0dyofW2SFAtS5syX45+aM3/82zRz5myv/Wf7dOUr5owkFed3mTMH26rMmbPz7f8UV/wPc2RM4kwIAOANJQQA8IYSAgB4QwkBALyhhAAA3lBCAABvKCEAgDeUEADAG0oIAOANJQQA8IYSAgB4QwkBALyJOOec70X8a8lkUvF4XEv1HeVG8nwvx6vo1+aYMxf+W585M3VduMcif3lkhjkTSfSaM7GpPeZM8kKhOSNJeXn9oXJWYYaeRuwzcJXJhPvZxqbYB5je+KVWcyadtQ/u7Pxuvjnz3o8rzRlJKkjYB5hW/seT5ky2u9ucGcsyrk+NelUdHR2aNu3KQ2o5EwIAeEMJAQC8MZfQgQMHtHz5cpWXlysSieiVV14ZcrtzTnV1dSovL1dhYaGWLl2q48ePD9d6AQATiLmEurq6NH/+fG3duvWytz/zzDPavHmztm7dqsOHD6usrEx33323Ojs7r3mxAICJxfysYE1NjWpqai57m3NOW7Zs0caNG7VixQpJ0o4dO1RaWqpdu3bpkUceubbVAgAmlGF9Tqi5uVmtra2qrq4evC4IAt155506ePDgZTOpVErJZHLIBQAwOQxrCbW2DrxEs7S0dMj1paWlg7ddrL6+XvF4fPBSUVExnEsCAIxhI/LquMhFb2hwzl1y3Wc2bNigjo6OwUtLS8tILAkAMAbZ3yl2BWVlZZIGzogSicTg9W1tbZecHX0mCAIFQTCcywAAjBPDeiZUVVWlsrIyNTQ0DF6XTqfV1NSkJUuWDOemAAATgPlM6MKFC/rggw8Gv25ubtY777yj4uJizZo1S+vXr9emTZs0e/ZszZ49W5s2bdKUKVP00EMPDevCAQDjn7mE3n77bS1btmzw69raWknSqlWr9Ktf/UpPPvmkenp69Nhjj+ncuXNatGiR3njjDcViseFbNQBgQmCAKUJr//6t5sxX/ukv5syfWhNXv9NF+tLhnu6M5toHmIYZLJobYjs5kdH7Ve3psQ8JvbniI3MmP8e+H84uOW/OYHQxwBQAMC5QQgAAbyghAIA3lBAAwBtKCADgDSUEAPCGEgIAeEMJAQC8oYQAAN5QQgAAbyghAIA3lBAAwBtKCADgzbB+siquIMSo5Ug0at9OiIxLpezbkTT9/yTNmbaV9o/0cC7EvsvJmjOSlJdnn+qcydj3eTYbYvR2iIeMuSH3Q5h93t5bZM7cNuOv5sxZjd50/Uju6PwT6TKZUdnOWMSZEADAG0oIAOANJQQA8IYSAgB4QwkBALyhhAAA3lBCAABvKCEAgDeUEADAG0oIAOANJQQA8IYSAgB4wwDT0eKcPRJmqGG/fQBnWNGOrlHZTl+ffUBoEPSF2laYYaTRqH1IaIjDQTkReygbYhCpJAUF9v13rrvQnLmQCcwZKdxQ1jBcmN+nMD/cSYwzIQCAN5QQAMAbSggA4A0lBADwhhICAHhDCQEAvKGEAADeUEIAAG8oIQCAN5QQAMAbSggA4A0lBADwhgGmE0wkN8+ccX3pUNtygX1bqX778Mlsn/2xUu6UcEMue0IMSy3Itw+57Ou3byfMANNMNtzjzKkFKXOmJ20/Ht44/VVzplx/NmdCi4TYf270hghPBJwJAQC8oYQAAN5QQgAAbyghAIA3lBAAwBtKCADgDSUEAPCGEgIAeEMJAQC8oYQAAN5QQgAAbyghAIA3DDBFaN1fvs6cSfUlzZncIGPOhDV1in1wZzozOr9GWRcxZ/Jzw+27VJ/9ewozYDXM9xSdc4M50/+Xv5ozkhTJsa/PhZudO2lxJgQA8IYSAgB4Yy6hAwcOaPny5SovL1ckEtErr7wy5PbVq1crEokMuSxevHi41gsAmEDMJdTV1aX58+dr69atn3ufe+65R2fOnBm87N2795oWCQCYmMzPPtbU1KimpuaK9wmCQGVlZaEXBQCYHEbkOaHGxkaVlJRozpw5evjhh9XW1va5902lUkomk0MuAIDJYdhLqKamRi+88IL279+vZ599VocPH9Zdd92lVOryL32tr69XPB4fvFRUVAz3kgAAY9Swv8Fh5cqVg3+eO3euFixYoMrKSu3Zs0crVqy45P4bNmxQbW3t4NfJZJIiAoBJYsTfZZdIJFRZWakTJ05c9vYgCBQEwUgvAwAwBo34+4Ta29vV0tKiRCIx0psCAIwz5jOhCxcu6IMPPhj8urm5We+8846Ki4tVXFysuro63X///UokEjp16pR+/OMfa/r06brvvvuGdeEAgPHPXEJvv/22li1bNvj1Z8/nrFq1Stu2bdOxY8e0c+dOnT9/XolEQsuWLdPu3bsVi8WGb9UAgAnBXEJLly6Vc58/qHDfvn3XtCBco1Gcnth6q/0pxdwQwz7z8/vNmWhOuP3Qm84zZ4oK0uZMT4jt9Gft/3s+tcA+kFWSkj0F5kxuiH0eZn3p6+PmTPQv5sg/B6P2TGb0Bu5OBMyOAwB4QwkBALyhhAAA3lBCAABvKCEAgDeUEADAG0oIAOANJQQA8IYSAgB4QwkBALyhhAAA3lBCAABvKCEAgDcj/smqGF2u3z5xOqy+ql57KGN/3FNUaJ+0XJAXbpJxmCna+bn2baUz9unMYaZoh1UU2CeDd/bYPyG5IL/PnGm/0T7hu+RNc2RA9vM/MQDDgzMhAIA3lBAAwBtKCADgDSUEAPCGEgIAeEMJAQC8oYQAAN5QQgAAbyghAIA3lBAAwBtKCADgDSUEAPCGAaZjWY59yKWy9gGmkbx8+3YklUxPmjPdKfu2nIuYM/ZEeFPz7MM+e0IMSs302x8zRiPhBnD2hthWTo59W6k++z9BydlZc6bEnBgwmgOBJyvOhAAA3lBCAABvKCEAgDeUEADAG0oIAOANJQQA8IYSAgB4QwkBALyhhAAA3lBCAABvKCEAgDeUEADAGwaYjmGRHPsYTmef7ajo9GJ7SNLZczFzpqzYPvT0XFehOTOjqMuckaS2Pvv3FM0JsdNDyI3at5MTcoBpXohtOWcf9pmfa89MreowZ0ILMRBYkRDjc124n9NEwJkQAMAbSggA4A0lBADwhhICAHhDCQEAvKGEAADeUEIAAG8oIQCAN5QQAMAbSggA4A0lBADwhhICAHjDANOxLDI6jxHS/5AIlYsV9ZgzYcY0FuT3mTNFeakQW5Kcsw+fnBpiW1PyC8yZrlS+OZMN8f1IUjzoNWfOZorMmXQmas/02f/ZigSBOSNJLmX/2Uai9u/JZTLmzETBmRAAwBtKCADgjamE6uvrtXDhQsViMZWUlOjee+/V+++/P+Q+zjnV1dWpvLxchYWFWrp0qY4fPz6siwYATAymEmpqatKaNWt06NAhNTQ0KJPJqLq6Wl1d//IBYs8884w2b96srVu36vDhwyorK9Pdd9+tzs7OYV88AGB8Mz3D9/rrrw/5evv27SopKdGRI0d0xx13yDmnLVu2aOPGjVqxYoUkaceOHSotLdWuXbv0yCOPDN/KAQDj3jU9J9TRMfAxu8XFAx8P3dzcrNbWVlVXVw/eJwgC3XnnnTp48OBl/45UKqVkMjnkAgCYHEKXkHNOtbW1uu222zR37lxJUmtrqySptLR0yH1LS0sHb7tYfX294vH44KWioiLskgAA40zoElq7dq3effdd/eY3v7nktkhk6HsTnHOXXPeZDRs2qKOjY/DS0tISdkkAgHEm1JtV161bp9dee00HDhzQzJkzB68vKyuTNHBGlEj8yxsg29raLjk7+kwQBApCvpEMADC+mc6EnHNau3atXnrpJe3fv19VVVVDbq+qqlJZWZkaGhoGr0un02pqatKSJUuGZ8UAgAnDdCa0Zs0a7dq1S6+++qpisdjg8zzxeFyFhYWKRCJav369Nm3apNmzZ2v27NnatGmTpkyZooceemhEvgEAwPhlKqFt27ZJkpYuXTrk+u3bt2v16tWSpCeffFI9PT167LHHdO7cOS1atEhvvPGGYrHYsCwYADBxmErIuauPn4xEIqqrq1NdXV3YNWGUtd9kH6YpSaWxNnPmbx1xc6Z8mv1l+1194Z5njOb2mzMFUfuA1esK7MNfwwww7enLM2ckaVbsnDnT1WdfX5jvqTBImzPRGdPNGUnKfPQ3e2iUBg9PFOwtAIA3lBAAwBtKCADgDSUEAPCGEgIAeEMJAQC8oYQAAN5QQgAAbyghAIA3lBAAwBtKCADgDSUEAPCGEgIAeBPqk1UxsaT+zeU/ev1qpuX3mjOn+orNmVlT7ROdT3TMMGckKTc3a85knf2xXG7Evp0gL2POdHQVmjOSdEPRWXPmTPc0cyaVsf8TlBu1TzrvmxVuinYkzBRtmHAmBADwhhICAHhDCQEAvKGEAADeUEIAAG8oIQCAN5QQAMAbSggA4A0lBADwhhICAHhDCQEAvKGEAADeMMB0LMsJN1jUqrvSPhhTki70BeZMJMS3VF5w3pw5+NGX7RuSVJDfFypnNavoU3OmJRk3Z/r6ouaMJFUF9gGmx4OEOdOVzjdnciLOnEnH7duRJPsRrlH7vZ0oOBMCAHhDCQEAvKGEAADeUEIAAG8oIQCAN5QQAMAbSggA4A0lBADwhhICAHhDCQEAvKGEAADeUEIAAG8YYAopGy52IW0f7zilIGXOdGQKzZmwgzuDPPsw10RBhzkzb0qLOfP77A3mTF5evzkTVm6O/UDq67c/Di7Itf+MQsw8DS0StR97o7i8MYczIQCAN5QQAMAbSggA4A0lBADwhhICAHhDCQEAvKGEAADeUEIAAG8oIQCAN5QQAMAbSggA4A0lBADwhgGmUE463GORvmyI4ZMhBoQeO1duzrgQa5Ok3nSeOTM1ah/K2uvyzZmOjinmTH5BnzkjSR+mppszuRH7ANNsyJ+TVW6P/bgLy/WP3tDYiYAzIQCAN5QQAMAbUwnV19dr4cKFisViKikp0b333qv3339/yH1Wr16tSCQy5LJ48eJhXTQAYGIwlVBTU5PWrFmjQ4cOqaGhQZlMRtXV1erq6hpyv3vuuUdnzpwZvOzdu3dYFw0AmBhML0x4/fXXh3y9fft2lZSU6MiRI7rjjjsGrw+CQGVlZcOzQgDAhHVNzwl1dAx8rHFxcfGQ6xsbG1VSUqI5c+bo4YcfVltb2+f+HalUSslkcsgFADA5hC4h55xqa2t12223ae7cuYPX19TU6IUXXtD+/fv17LPP6vDhw7rrrruUSl3+Zaz19fWKx+ODl4qKirBLAgCMM6HfJ7R27Vq9++67euutt4Zcv3LlysE/z507VwsWLFBlZaX27NmjFStWXPL3bNiwQbW1tYNfJ5NJiggAJolQJbRu3Tq99tprOnDggGbOnHnF+yYSCVVWVurEiROXvT0IAgVBEGYZAIBxzlRCzjmtW7dOL7/8shobG1VVVXXVTHt7u1paWpRIJEIvEgAwMZmeE1qzZo1+/etfa9euXYrFYmptbVVra6t6enokSRcuXNATTzyhP/zhDzp16pQaGxu1fPlyTZ8+Xffdd9+IfAMAgPHLdCa0bds2SdLSpUuHXL99+3atXr1a0WhUx44d086dO3X+/HklEgktW7ZMu3fvViwWG7ZFAwAmBvN/x11JYWGh9u3bd00LAgBMHkzRhq674dNQuYrYeXOmO2OfHv2VqZ/YM7F2c0aSpuX2mDMLik6aM7Pz7OvbWznPnPnH61rMGUl6asafzZm1afv/dkyf2nX1O10kR1d+MHxZKSZbj1UMMAUAeEMJAQC8oYQAAN5QQgAAbyghAIA3lBAAwBtKCADgDSUEAPCGEgIAeEMJAQC8oYQAAN5QQgAAbxhgOpb1j87QxQvvfClU7vCXrjNngrP2Q645dfUPT7xYwSchhlxKioTY5f8rsdic6S2zb6j4Hftjxg+DG8wZSfp1xZ3mTCTEdqLdIVLzOs2Rr3zYZt+OpEyY0Cj93k4UnAkBALyhhAAA3lBCAABvKCEAgDeUEADAG0oIAOANJQQA8IYSAgB4QwkBALyhhAAA3lBCAABvxtzsOOcGZn5l1CeFG/81YUScfQc4Z592le3tNWckKdtjn5HV3xvikEvbI/3p0Zsd15+yzz8Lte/S9seM/ZEwE92kbK99/4XaUoh9p2778ZrJhjiIJGVcnzkzWr+3Y1lGA/vNfYF9EXFf5F6j6KOPPlJFRYXvZQAArlFLS4tmzpx5xfuMuRLKZrP6+OOPFYvFFLnoUVwymVRFRYVaWlo0bdo0Tyv0j/0wgP0wgP0wgP0wYCzsB+ecOjs7VV5erpycK5/Bj7n/jsvJyblqc06bNm1SH2SfYT8MYD8MYD8MYD8M8L0f4vH4F7ofL0wAAHhDCQEAvBlXJRQEgZ566ikFQeB7KV6xHwawHwawHwawHwaMt/0w5l6YAACYPMbVmRAAYGKhhAAA3lBCAABvKCEAgDfjqoSee+45VVVVqaCgQLfccot+//vf+17SqKqrq1MkEhlyKSsr872sEXfgwAEtX75c5eXlikQieuWVV4bc7pxTXV2dysvLVVhYqKVLl+r48eN+FjuCrrYfVq9efcnxsXjxYj+LHSH19fVauHChYrGYSkpKdO+99+r9998fcp/JcDx8kf0wXo6HcVNCu3fv1vr167Vx40YdPXpUt99+u2pqanT69GnfSxtVN910k86cOTN4OXbsmO8ljbiuri7Nnz9fW7duveztzzzzjDZv3qytW7fq8OHDKisr0913363Ozs5RXunIutp+kKR77rlnyPGxd+/eUVzhyGtqatKaNWt06NAhNTQ0KJPJqLq6Wl1dXYP3mQzHwxfZD9I4OR7cOPHNb37TPfroo0Ou++pXv+p+9KMfeVrR6Hvqqafc/PnzfS/DK0nu5ZdfHvw6m826srIy9/TTTw9e19vb6+LxuPv5z3/uYYWj4+L94Jxzq1atct/5zne8rMeXtrY2J8k1NTU55ybv8XDxfnBu/BwP4+JMKJ1O68iRI6qurh5yfXV1tQ4ePOhpVX6cOHFC5eXlqqqq0gMPPKCTJ0/6XpJXzc3Nam1tHXJsBEGgO++8c9IdG5LU2NiokpISzZkzRw8//LDa2tp8L2lEdXR0SJKKi4slTd7j4eL98JnxcDyMixL65JNP1N/fr9LS0iHXl5aWqrW11dOqRt+iRYu0c+dO7du3T88//7xaW1u1ZMkStbe3+16aN5/9/Cf7sSFJNTU1euGFF7R//349++yzOnz4sO666y6lUinfSxsRzjnV1tbqtttu09y5cyVNzuPhcvtBGj/Hw5ibon0lF3+0g3PukusmspqamsE/z5s3T7feeqtuuOEG7dixQ7W1tR5X5t9kPzYkaeXKlYN/njt3rhYsWKDKykrt2bNHK1as8LiykbF27Vq9++67euutty65bTIdD5+3H8bL8TAuzoSmT5+uaDR6ySOZtra2Sx7xTCZFRUWaN2+eTpw44Xsp3nz26kCOjUslEglVVlZOyONj3bp1eu211/Tmm28O+eiXyXY8fN5+uJyxejyMixLKz8/XLbfcooaGhiHXNzQ0aMmSJZ5W5V8qldJ7772nRCLheyneVFVVqaysbMixkU6n1dTUNKmPDUlqb29XS0vLhDo+nHNau3atXnrpJe3fv19VVVVDbp8sx8PV9sPljNnjweOLIkxefPFFl5eX5375y1+6P//5z279+vWuqKjInTp1yvfSRs3jjz/uGhsb3cmTJ92hQ4fct7/9bReLxSb8Pujs7HRHjx51R48edZLc5s2b3dGjR92HH37onHPu6aefdvF43L300kvu2LFj7sEHH3SJRMIlk0nPKx9eV9oPnZ2d7vHHH3cHDx50zc3N7s0333S33nqru/766yfUfvjhD3/o4vG4a2xsdGfOnBm8dHd3D95nMhwPV9sP4+l4GDcl5JxzP/vZz1xlZaXLz8933/jGN4a8HHEyWLlypUskEi4vL8+Vl5e7FStWuOPHj/te1oh78803naRLLqtWrXLODbws96mnnnJlZWUuCAJ3xx13uGPHjvld9Ai40n7o7u521dXVbsaMGS4vL8/NmjXLrVq1yp0+fdr3sofV5b5/SW779u2D95kMx8PV9sN4Oh74KAcAgDfj4jkhAMDERAkBALyhhAAA3lBCAABvKCEAgDeUEADAG0oIAOANJQQA8IYSAgB4QwkBALyhhAAA3lBCAABv/j9xZ9+nrMm5bQAAAABJRU5ErkJggg=="},"metadata":{}}]},{"cell_type":"code","source":"train_labels[10]","metadata":{"execution":{"iopub.status.busy":"2023-07-24T10:17:01.791632Z","iopub.execute_input":"2023-07-24T10:17:01.792046Z","iopub.status.idle":"2023-07-24T10:17:01.799038Z","shell.execute_reply.started":"2023-07-24T10:17:01.792013Z","shell.execute_reply":"2023-07-24T10:17:01.797734Z"},"trusted":true},"execution_count":9,"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"code","source":"class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', \n               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']","metadata":{"execution":{"iopub.status.busy":"2023-07-24T10:18:38.769125Z","iopub.execute_input":"2023-07-24T10:18:38.769533Z","iopub.status.idle":"2023-07-24T10:18:38.774892Z","shell.execute_reply.started":"2023-07-24T10:18:38.769502Z","shell.execute_reply":"2023-07-24T10:18:38.773545Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"code","source":"plt.imshow(train_data[17], cmap = plt.cm.binary)\nplt.title(class_names[train_labels[17]])\nplt.axis(False)","metadata":{"execution":{"iopub.status.busy":"2023-07-24T10:21:04.733265Z","iopub.execute_input":"2023-07-24T10:21:04.734049Z","iopub.status.idle":"2023-07-24T10:21:04.901577Z","shell.execute_reply.started":"2023-07-24T10:21:04.734003Z","shell.execute_reply":"2023-07-24T10:21:04.900633Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"(-0.5, 27.5, 27.5, -0.5)"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"import random \nplt.figure(figsize=(7, 7))\nfor i in range(4):\n    ax = plt.subplot(2, 2, i + 1)\n    rand_index = random.choice(range(len(train_data)))\n    plt.imshow(train_data[rand_index], cmap = plt.cm.binary)\n    plt.title(class_names[train_labels[rand_index]])\n    plt.axis(False)","metadata":{"execution":{"iopub.status.busy":"2023-07-24T10:30:50.514188Z","iopub.execute_input":"2023-07-24T10:30:50.515120Z","iopub.status.idle":"2023-07-24T10:30:50.866355Z","shell.execute_reply.started":"2023-07-24T10:30:50.515083Z","shell.execute_reply":"2023-07-24T10:30:50.865490Z"},"trusted":true},"execution_count":26,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 700x700 with 4 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"# normilization \ntrain_data = train_data/ 255\ntest_data = test_data / 255 \n\ntrain_data.min(), train_data.max()","metadata":{"execution":{"iopub.status.busy":"2023-07-24T10:34:48.404085Z","iopub.execute_input":"2023-07-24T10:34:48.404466Z","iopub.status.idle":"2023-07-24T10:34:48.694421Z","shell.execute_reply.started":"2023-07-24T10:34:48.404435Z","shell.execute_reply":"2023-07-24T10:34:48.693289Z"},"trusted":true},"execution_count":28,"outputs":[{"execution_count":28,"output_type":"execute_result","data":{"text/plain":"(0.0, 1.0)"},"metadata":{}}]},{"cell_type":"code","source":"model_1 = tf.keras.Sequential([\n  tf.keras.layers.Flatten(input_shape=(28, 28)),\n  tf.keras.layers.Dense(4, activation=\"relu\"),\n  tf.keras.layers.Dense(10, activation=\"softmax\") \n])\n\nmodel_1.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n                 optimizer=tf.keras.optimizers.Adam(),\n                 metrics=[\"accuracy\"])\n\n\nnorm_history = model_1.fit(train_data,\n                            train_labels,\n                            epochs=10,\n                            validation_data=(test_data, test_labels))","metadata":{"execution":{"iopub.status.busy":"2023-07-24T10:45:26.373853Z","iopub.execute_input":"2023-07-24T10:45:26.374304Z","iopub.status.idle":"2023-07-24T10:46:01.713607Z","shell.execute_reply.started":"2023-07-24T10:45:26.374269Z","shell.execute_reply":"2023-07-24T10:46:01.712453Z"},"trusted":true},"execution_count":32,"outputs":[{"name":"stdout","text":"Epoch 1/10\n1875/1875 [==============================] - 4s 2ms/step - loss: 1.0760 - accuracy: 0.5884 - val_loss: 0.7832 - val_accuracy: 0.7205\nEpoch 2/10\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.6387 - accuracy: 0.7785 - val_loss: 0.6038 - val_accuracy: 0.7928\nEpoch 3/10\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.5651 - accuracy: 0.8055 - val_loss: 0.5706 - val_accuracy: 0.8038\nEpoch 4/10\n1875/1875 [==============================] - 4s 2ms/step - loss: 0.5446 - accuracy: 0.8124 - val_loss: 0.5571 - val_accuracy: 0.8064\nEpoch 5/10\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.5313 - accuracy: 0.8181 - val_loss: 0.5537 - val_accuracy: 0.8074\nEpoch 6/10\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.5234 - accuracy: 0.8195 - val_loss: 0.5452 - val_accuracy: 0.8118\nEpoch 7/10\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.5159 - accuracy: 0.8224 - val_loss: 0.5615 - val_accuracy: 0.8053\nEpoch 8/10\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.5113 - accuracy: 0.8226 - val_loss: 0.5447 - val_accuracy: 0.8108\nEpoch 9/10\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.5062 - accuracy: 0.8248 - val_loss: 0.5370 - val_accuracy: 0.8141\nEpoch 10/10\n1875/1875 [==============================] - 4s 2ms/step - loss: 0.5017 - accuracy: 0.8261 - val_loss: 0.5357 - val_accuracy: 0.8169\n","output_type":"stream"}]},{"cell_type":"code","source":"import pandas as pd \npd.DataFrame(norm_history.history).plot()","metadata":{"execution":{"iopub.status.busy":"2023-07-24T10:46:32.784313Z","iopub.execute_input":"2023-07-24T10:46:32.785292Z","iopub.status.idle":"2023-07-24T10:46:33.104468Z","shell.execute_reply.started":"2023-07-24T10:46:32.785254Z","shell.execute_reply":"2023-07-24T10:46:33.102118Z"},"trusted":true},"execution_count":33,"outputs":[{"execution_count":33,"output_type":"execute_result","data":{"text/plain":"<Axes: >"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}]},{"cell_type":"code","source":"# let's find a learning rate\n\nmodel_2 = tf.keras.Sequential([\n  tf.keras.layers.Flatten(input_shape=(28, 28)), \n  tf.keras.layers.Dense(4, activation=\"relu\"),\n  tf.keras.layers.Dense(4, activation=\"relu\"),\n  tf.keras.layers.Dense(10, activation=\"softmax\") \n])\n\n# Compile the model\nmodel_2.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n                 optimizer=tf.keras.optimizers.Adam(),\n                 metrics=[\"accuracy\"])\n\nlr = tf.keras.callbacks.LearningRateScheduler(lambda epoch: 1e-3 * 10**(epoch/20))\n\nhistory_2 = model_2.fit(train_data, \n                       train_labels, \n                       epochs = 40, \n                       callbacks = [lr],\n                       validation_data=(test_data, test_labels))","metadata":{"execution":{"iopub.status.busy":"2023-07-24T10:58:20.232795Z","iopub.execute_input":"2023-07-24T10:58:20.233262Z","iopub.status.idle":"2023-07-24T11:00:43.170696Z","shell.execute_reply.started":"2023-07-24T10:58:20.233227Z","shell.execute_reply":"2023-07-24T11:00:43.169495Z"},"trusted":true},"execution_count":40,"outputs":[{"name":"stdout","text":"Epoch 1/40\n1875/1875 [==============================] - 4s 2ms/step - loss: 1.3949 - accuracy: 0.4703 - val_loss: 1.0560 - val_accuracy: 0.6396 - lr: 0.0010\nEpoch 2/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.8998 - accuracy: 0.6739 - val_loss: 0.8326 - val_accuracy: 0.6811 - lr: 0.0011\nEpoch 3/40\n1875/1875 [==============================] - 4s 2ms/step - loss: 0.7956 - accuracy: 0.6928 - val_loss: 0.7922 - val_accuracy: 0.7026 - lr: 0.0013\nEpoch 4/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.7347 - accuracy: 0.7411 - val_loss: 0.7117 - val_accuracy: 0.7537 - lr: 0.0014\nEpoch 5/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.6812 - accuracy: 0.7625 - val_loss: 0.7118 - val_accuracy: 0.7533 - lr: 0.0016\nEpoch 6/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.6590 - accuracy: 0.7694 - val_loss: 0.6919 - val_accuracy: 0.7510 - lr: 0.0018\nEpoch 7/40\n1875/1875 [==============================] - 4s 2ms/step - loss: 0.6457 - accuracy: 0.7724 - val_loss: 0.6486 - val_accuracy: 0.7720 - lr: 0.0020\nEpoch 8/40\n1875/1875 [==============================] - 4s 2ms/step - loss: 0.6328 - accuracy: 0.7758 - val_loss: 0.6639 - val_accuracy: 0.7580 - lr: 0.0022\nEpoch 9/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.6277 - accuracy: 0.7779 - val_loss: 0.6479 - val_accuracy: 0.7684 - lr: 0.0025\nEpoch 10/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.6216 - accuracy: 0.7782 - val_loss: 0.6387 - val_accuracy: 0.7768 - lr: 0.0028\nEpoch 11/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.6142 - accuracy: 0.7824 - val_loss: 0.6569 - val_accuracy: 0.7701 - lr: 0.0032\nEpoch 12/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.6082 - accuracy: 0.7847 - val_loss: 0.6445 - val_accuracy: 0.7737 - lr: 0.0035\nEpoch 13/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.6026 - accuracy: 0.7887 - val_loss: 0.6468 - val_accuracy: 0.7723 - lr: 0.0040\nEpoch 14/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.5977 - accuracy: 0.7909 - val_loss: 0.6348 - val_accuracy: 0.7818 - lr: 0.0045\nEpoch 15/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.5981 - accuracy: 0.7917 - val_loss: 0.6439 - val_accuracy: 0.7731 - lr: 0.0050\nEpoch 16/40\n1875/1875 [==============================] - 4s 2ms/step - loss: 0.5922 - accuracy: 0.7957 - val_loss: 0.6371 - val_accuracy: 0.7793 - lr: 0.0056\nEpoch 17/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.5960 - accuracy: 0.7936 - val_loss: 0.6544 - val_accuracy: 0.7714 - lr: 0.0063\nEpoch 18/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.5991 - accuracy: 0.7929 - val_loss: 0.6341 - val_accuracy: 0.7774 - lr: 0.0071\nEpoch 19/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.5981 - accuracy: 0.7918 - val_loss: 0.6660 - val_accuracy: 0.7677 - lr: 0.0079\nEpoch 20/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.5987 - accuracy: 0.7932 - val_loss: 0.6192 - val_accuracy: 0.7889 - lr: 0.0089\nEpoch 21/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.6046 - accuracy: 0.7904 - val_loss: 0.6977 - val_accuracy: 0.7659 - lr: 0.0100\nEpoch 22/40\n1875/1875 [==============================] - 4s 2ms/step - loss: 0.6042 - accuracy: 0.7891 - val_loss: 0.6477 - val_accuracy: 0.7763 - lr: 0.0112\nEpoch 23/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.6116 - accuracy: 0.7883 - val_loss: 0.6212 - val_accuracy: 0.7887 - lr: 0.0126\nEpoch 24/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.6218 - accuracy: 0.7862 - val_loss: 0.6591 - val_accuracy: 0.7728 - lr: 0.0141\nEpoch 25/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.6324 - accuracy: 0.7808 - val_loss: 0.6496 - val_accuracy: 0.7769 - lr: 0.0158\nEpoch 26/40\n1875/1875 [==============================] - 4s 2ms/step - loss: 0.6297 - accuracy: 0.7838 - val_loss: 0.6501 - val_accuracy: 0.7685 - lr: 0.0178\nEpoch 27/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.6444 - accuracy: 0.7772 - val_loss: 0.7033 - val_accuracy: 0.7713 - lr: 0.0200\nEpoch 28/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.6554 - accuracy: 0.7750 - val_loss: 0.7303 - val_accuracy: 0.7606 - lr: 0.0224\nEpoch 29/40\n1875/1875 [==============================] - 4s 2ms/step - loss: 0.6676 - accuracy: 0.7686 - val_loss: 0.7433 - val_accuracy: 0.7483 - lr: 0.0251\nEpoch 30/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.6826 - accuracy: 0.7663 - val_loss: 0.7157 - val_accuracy: 0.7673 - lr: 0.0282\nEpoch 31/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.7040 - accuracy: 0.7593 - val_loss: 0.7005 - val_accuracy: 0.7576 - lr: 0.0316\nEpoch 32/40\n1875/1875 [==============================] - 4s 2ms/step - loss: 0.7160 - accuracy: 0.7538 - val_loss: 0.6528 - val_accuracy: 0.7869 - lr: 0.0355\nEpoch 33/40\n1875/1875 [==============================] - 4s 2ms/step - loss: 0.7493 - accuracy: 0.7412 - val_loss: 0.8487 - val_accuracy: 0.6720 - lr: 0.0398\nEpoch 34/40\n1875/1875 [==============================] - 4s 2ms/step - loss: 0.7675 - accuracy: 0.7335 - val_loss: 0.9065 - val_accuracy: 0.7151 - lr: 0.0447\nEpoch 35/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.8111 - accuracy: 0.7175 - val_loss: 0.8165 - val_accuracy: 0.7371 - lr: 0.0501\nEpoch 36/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.8868 - accuracy: 0.6818 - val_loss: 0.8940 - val_accuracy: 0.6483 - lr: 0.0562\nEpoch 37/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.9763 - accuracy: 0.6351 - val_loss: 0.9716 - val_accuracy: 0.6390 - lr: 0.0631\nEpoch 38/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 1.0334 - accuracy: 0.6092 - val_loss: 1.0979 - val_accuracy: 0.6008 - lr: 0.0708\nEpoch 39/40\n1875/1875 [==============================] - 4s 2ms/step - loss: 1.1371 - accuracy: 0.5638 - val_loss: 1.1876 - val_accuracy: 0.5473 - lr: 0.0794\nEpoch 40/40\n1875/1875 [==============================] - 4s 2ms/step - loss: 1.1862 - accuracy: 0.5464 - val_loss: 1.0161 - val_accuracy: 0.5734 - lr: 0.0891\n","output_type":"stream"}]},{"cell_type":"code","source":"import numpy as np \nlrs = 1e-3 * (10**(np.arange(40)/20))\nplt.semilogx(lrs, history_2.history[\"loss\"])\nplt.xlabel(\"Learning rate\")\nplt.ylabel(\"Loss\")\nplt.title(\"The ideal learning rate\");","metadata":{"execution":{"iopub.status.busy":"2023-07-24T11:01:09.867731Z","iopub.execute_input":"2023-07-24T11:01:09.868160Z","iopub.status.idle":"2023-07-24T11:01:10.439532Z","shell.execute_reply.started":"2023-07-24T11:01:09.868126Z","shell.execute_reply":"2023-07-24T11:01:10.438224Z"},"trusted":true},"execution_count":42,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":" lr seems to be the best at  0.0089. Let's see","metadata":{}},{"cell_type":"code","source":"model_3 = tf.keras.Sequential([\n  tf.keras.layers.Flatten(input_shape=(28, 28)), \n  tf.keras.layers.Dense(4, activation=\"relu\"),\n  tf.keras.layers.Dense(4, activation=\"relu\"),\n  tf.keras.layers.Dense(10, activation=\"softmax\") \n])\n\n# Compile the model\nmodel_3.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n                 optimizer=tf.keras.optimizers.Adam(learning_rate = 0.0089),\n                 metrics=[\"accuracy\"])\n\nhistory_3 = model_2.fit(train_data, \n                       train_labels, \n                       epochs = 40, \n                       validation_data=(test_data, test_labels))","metadata":{"execution":{"iopub.status.busy":"2023-07-24T11:17:23.128708Z","iopub.execute_input":"2023-07-24T11:17:23.129276Z","iopub.status.idle":"2023-07-24T11:19:41.929125Z","shell.execute_reply.started":"2023-07-24T11:17:23.129228Z","shell.execute_reply":"2023-07-24T11:19:41.927846Z"},"trusted":true},"execution_count":58,"outputs":[{"name":"stdout","text":"Epoch 1/40\n1875/1875 [==============================] - 4s 2ms/step - loss: 1.7300 - accuracy: 0.1951 - val_loss: 1.7318 - val_accuracy: 0.1960\nEpoch 2/40\n1875/1875 [==============================] - 4s 2ms/step - loss: 1.7299 - accuracy: 0.1972 - val_loss: 1.7259 - val_accuracy: 0.1885\nEpoch 3/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 1.7297 - accuracy: 0.1983 - val_loss: 1.7332 - val_accuracy: 0.1961\nEpoch 4/40\n1875/1875 [==============================] - 4s 2ms/step - loss: 1.7314 - accuracy: 0.1935 - val_loss: 1.7420 - val_accuracy: 0.1978\nEpoch 5/40\n1875/1875 [==============================] - 4s 2ms/step - loss: 1.7326 - accuracy: 0.1943 - val_loss: 1.7384 - val_accuracy: 0.1982\nEpoch 6/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 1.7307 - accuracy: 0.1957 - val_loss: 1.7350 - val_accuracy: 0.1955\nEpoch 7/40\n1875/1875 [==============================] - 4s 2ms/step - loss: 1.7316 - accuracy: 0.1950 - val_loss: 1.7426 - val_accuracy: 0.1961\nEpoch 8/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 1.7305 - accuracy: 0.1959 - val_loss: 1.7368 - val_accuracy: 0.1960\nEpoch 9/40\n1875/1875 [==============================] - 4s 2ms/step - loss: 1.7298 - accuracy: 0.1998 - val_loss: 1.7511 - val_accuracy: 0.1998\nEpoch 10/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 1.7294 - accuracy: 0.1988 - val_loss: 1.7249 - val_accuracy: 0.1976\nEpoch 11/40\n1875/1875 [==============================] - 4s 2ms/step - loss: 1.7300 - accuracy: 0.1957 - val_loss: 1.7314 - val_accuracy: 0.1982\nEpoch 12/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 1.7305 - accuracy: 0.1920 - val_loss: 1.7422 - val_accuracy: 0.1982\nEpoch 13/40\n1875/1875 [==============================] - 4s 2ms/step - loss: 1.7301 - accuracy: 0.1984 - val_loss: 1.7419 - val_accuracy: 0.1982\nEpoch 14/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 1.7303 - accuracy: 0.1995 - val_loss: 1.7674 - val_accuracy: 0.1988\nEpoch 15/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 1.7313 - accuracy: 0.2004 - val_loss: 1.7238 - val_accuracy: 0.1879\nEpoch 16/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 1.7290 - accuracy: 0.1987 - val_loss: 1.7297 - val_accuracy: 0.1965\nEpoch 17/40\n1875/1875 [==============================] - 4s 2ms/step - loss: 1.7308 - accuracy: 0.1957 - val_loss: 1.7295 - val_accuracy: 0.1977\nEpoch 18/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 1.7300 - accuracy: 0.2004 - val_loss: 1.7307 - val_accuracy: 0.1972\nEpoch 19/40\n1875/1875 [==============================] - 4s 2ms/step - loss: 1.7312 - accuracy: 0.1971 - val_loss: 1.7459 - val_accuracy: 0.1994\nEpoch 20/40\n1875/1875 [==============================] - 4s 2ms/step - loss: 1.7307 - accuracy: 0.1966 - val_loss: 1.7313 - val_accuracy: 0.1998\nEpoch 21/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 1.7305 - accuracy: 0.1959 - val_loss: 1.7250 - val_accuracy: 0.1955\nEpoch 22/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 1.7311 - accuracy: 0.1981 - val_loss: 1.7504 - val_accuracy: 0.1955\nEpoch 23/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 1.7321 - accuracy: 0.1974 - val_loss: 1.7390 - val_accuracy: 0.1992\nEpoch 24/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 1.7304 - accuracy: 0.1968 - val_loss: 1.7293 - val_accuracy: 0.1972\nEpoch 25/40\n1875/1875 [==============================] - 4s 2ms/step - loss: 1.7315 - accuracy: 0.1963 - val_loss: 1.7503 - val_accuracy: 0.1875\nEpoch 26/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 1.7306 - accuracy: 0.1954 - val_loss: 1.7441 - val_accuracy: 0.1978\nEpoch 27/40\n1875/1875 [==============================] - 4s 2ms/step - loss: 1.7311 - accuracy: 0.1964 - val_loss: 1.7263 - val_accuracy: 0.1879\nEpoch 28/40\n1875/1875 [==============================] - 4s 2ms/step - loss: 1.7299 - accuracy: 0.1947 - val_loss: 1.7287 - val_accuracy: 0.1976\nEpoch 29/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 1.7310 - accuracy: 0.1938 - val_loss: 1.7314 - val_accuracy: 0.1972\nEpoch 30/40\n1875/1875 [==============================] - 4s 2ms/step - loss: 1.7318 - accuracy: 0.1960 - val_loss: 1.7276 - val_accuracy: 0.1972\nEpoch 31/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 1.7300 - accuracy: 0.1995 - val_loss: 1.7382 - val_accuracy: 0.1885\nEpoch 32/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 1.7295 - accuracy: 0.1958 - val_loss: 1.7312 - val_accuracy: 0.1982\nEpoch 33/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 1.7302 - accuracy: 0.2000 - val_loss: 1.7278 - val_accuracy: 0.1993\nEpoch 34/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 1.7303 - accuracy: 0.1966 - val_loss: 1.7360 - val_accuracy: 0.1960\nEpoch 35/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 1.7311 - accuracy: 0.1952 - val_loss: 1.7474 - val_accuracy: 0.1959\nEpoch 36/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 1.7303 - accuracy: 0.1984 - val_loss: 1.7304 - val_accuracy: 0.1993\nEpoch 37/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 1.7310 - accuracy: 0.1975 - val_loss: 1.7467 - val_accuracy: 0.1993\nEpoch 38/40\n1875/1875 [==============================] - 4s 2ms/step - loss: 1.7299 - accuracy: 0.1970 - val_loss: 1.7262 - val_accuracy: 0.1972\nEpoch 39/40\n1875/1875 [==============================] - 3s 2ms/step - loss: 1.7316 - accuracy: 0.1963 - val_loss: 1.7305 - val_accuracy: 0.1875\nEpoch 40/40\n1875/1875 [==============================] - 4s 2ms/step - loss: 1.7317 - accuracy: 0.1951 - val_loss: 1.7415 - val_accuracy: 0.1982\n","output_type":"stream"}]},{"cell_type":"code","source":"pd.DataFrame(history_3.history).plot()","metadata":{"execution":{"iopub.status.busy":"2023-07-24T11:19:44.812837Z","iopub.execute_input":"2023-07-24T11:19:44.813995Z","iopub.status.idle":"2023-07-24T11:19:45.171169Z","shell.execute_reply.started":"2023-07-24T11:19:44.813939Z","shell.execute_reply":"2023-07-24T11:19:45.170036Z"},"trusted":true},"execution_count":59,"outputs":[{"execution_count":59,"output_type":"execute_result","data":{"text/plain":"<Axes: >"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAiMAAAGdCAYAAADAAnMpAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAABU0klEQVR4nO3deXwV5aH/8c+c/WRPCAkJSwQhyq6Cslg3qCi2VIqtWKiA1l+l1aqldeHauvTaor6qVcvFqlWsLS61ivUWqmJdQKheRFIRUFkCAQnEBMies83z++MkR8KahMCY+H3nNa9ZzizPnJmc+c4zc+ZYxhiDiIiIiENcThdAREREvtoURkRERMRRCiMiIiLiKIURERERcZTCiIiIiDhKYUREREQcpTAiIiIijlIYEREREUd5nC5AS9i2zY4dO0hNTcWyLKeLIyIiIi1gjKG6upr8/HxcrkPXf3SIMLJjxw569uzpdDFERESkDbZt20aPHj0O+XqHCCOpqalAfGXS0tIcLo2IiIi0RFVVFT179kwcxw+lQ4SRpkszaWlpCiMiIiIdzJFusdANrCIiIuIohRERERFxVKvDyNKlS5kwYQL5+flYlsVLL710xGkWLFjA0KFDSUpKIi8vjyuuuIKKioq2lFdEREQ6mVaHkdraWoYOHcrcuXNbNP4777zDtGnT+MEPfsDatWt5/vnnWblyJVdddVWrCysiIiKdT6tvYB0/fjzjx49v8fjvvvsuJ5xwAtdddx0AvXv35uqrr+bee+9t7aJFRESkEzrm94yMHj2a7du3s3jxYowx7Nq1i7/97W984xvfONaLFhERkQ7guISRBQsWMHnyZHw+H926dSMjI4Pf//73h5wmFApRVVXVrBEREZHO6ZiHkXXr1nHddddx2223sWrVKl555RWKi4uZOXPmIaeZM2cO6enpiUZPXxUREem8LGOMafPElsXChQuZOHHiIce5/PLLaWho4Pnnn08Me+eddzjrrLPYsWMHeXl5B0wTCoUIhUKJ/qYnuFVWVuqhZyIiIh1EVVUV6enpRzx+H/MnsNbV1eHxNF+M2+0G4j+gczB+vx+/33+siyYiIiJfAq2+TFNTU0NRURFFRUUAFBcXU1RURElJCQCzZ89m2rRpifEnTJjAiy++yMMPP8zmzZtZvnw51113HWeccQb5+fntsxYiIiLSYbW6ZuT999/nvPPOS/TPmjULgOnTp/Pkk09SWlqaCCYAM2bMoLq6mrlz5/Kzn/2MjIwMxowZwz333NMOxRcREZGO7qjuGTleWnrNSeTLYFv1NhZtXsTo/NEM6TrE6eKIiDjmS3PPiMhXxSe7P+Hxjx7n1S2vYhubR/7zCDefcTOTT5p8xF+sFBH5KlMYETkKxhhW7VrF4x89zjufvZMYfkLaCWyp2sKv3/s16yrWcevIW/G7dVO2iMjBKIzIcVUfrcfn8uF2uZ0uylGxjc1b297i8Y8e58PPPwTAZbm4oOACrhh0BSdnncyTa5/kgQ8eYOHGhWzcu5Hfnfs7cpNznS24iMiXkO4ZkWMqFAtRVFbEu6Xv8l7pe6ytWEumP5MZA2dw6UmXkuRNcrqIrRKJRVhUvIj5H81nc+VmAHwuH9/u922mD5hOz7TmD+hbsWMFN759I1XhKroEunD/ufdzWu5pThS9UzLG8M5n7/Dk2icpriymMKuQwdmDGZw9mIFdBtIl2OW4lWVn7U7+vvHvLC5eTEO0gW7J3chNzqVbcje6JXUjLzkv3p3cjQx/hi7dfckZY9hWvY1t1dsY0GUAmYHMdp9/eX05WYGsDn9ydjgtPX4rjHwJGGMoqyujNlpLmi+NNF8aPrfvuJcjZscorS3ls5rP8Lv95Cblkp2UjdflbdU81u9ez7ul7/Ju6bsUlRURioUOOm66P52p/acy5eQppPvT22s1WqQ+Ws+Wyi1E7SiWZcWbpj9rv3Zj94odK/jT2j+xq24XAKneVCafPJmp/aeSHcw+5LK2VW/j+jevZ8OeDXgsT4e+jyRmx1hXsY7lO5az/LPlfLz7YwozCxmZP5JReaMY2nUoXnfL95e2itpRXt3yKk989ASf7vn0kOPlJ+czMHsgg7MHMyh7EAO6DCDZm9xu5YjEIry57U0WblzIih0rsI3doukC7kA8qCR1o3tqd87MP5Ovdf9ahwvnnUl5fTkflX/EmvI1fFT+ER+Vf0RVOP5TJC7LxbDcYYzpOYbzep1H95TubVpGOBZm5c6VvLXtLZZuX8qO2h3kBHOYcOIEvtX3W/RJ79OOa9Ryxhh21O6gS6ALAU+gXeetMPIlZBub0tpSNu3dxOa9m9lcuZlNlfHumkhNs3GDniCpvtREOEn3p8e7/WmJYU3dqb5UUr2p8fH9aQTcgUMe6CKxCNtrticSf0lVCSXVJWyv3s72mu1E7Wiz8S0ssgJZ5CTlkJuUS25yLjlJOYkmNyl+2WHlzpW8W/ou/7fz/6gOVzebR9dgV0bmjWRE3giG5Q5j5c6V/HHNHympjn8FPNmbzGUnXcblAy4/JmeydZE6Pt79Met3r2ddxTrWVaxjc+XmFh849pcdzGbagGl8t/C7pPhSWlyG21bcxqtbXgVgUr9J3DriVkdCZ2uV15ezYscK3vnsHf6949/sDe095LhBT5DTu53OqLxRjMofRZ/0Pu0auhqiDby08SWeXPskn9V8BkCSJ4nvFn6Xc3uey8a9G1lTvoa15WvZXLkZQ/OPNwuLEzNOZGCXgfTv0p/CzEIKMwtbHYY37tnIixtf5B+b/sGe0J7E8OG5w5nUbxI9U3uys24nO2t2xtu1XzQVDRUHnWfAHeCsHmfx9V5f5+weZ7d43/qyqwnX8PHujylvKCcvOY8eKT3ICmQ5GsZrwjWsq1gX31cq1rKmfA07a3ceMJ7P5SMnKYftNdubDT8562TG9BzDmF5jKMwsPOy6lNeXs2z7Mt7e/jYrdqygPlp/yHGHdB3CxL4TufCEC0n1pbZ9BQ/DGMOuul2sLV/L2oq1rKtYx9qKtewN7eXR8x9lVP6odl2ewkgL1EXq8Lv97V5FVhep47OazyipLqG4sphNezexae8mtlRtOeSO6LbcJHmTqAnXHPAB2loel+eAkGJjs716O6W1pYc9CHtdXrqndCccC1NWX3ZAOGmJVG8qw7sNZ2TeSEbmjaR3eu8D/lljdozXtr7Gox8+ysa9G4H4h/F3Cr/D9IHT6ZbcrdXLBagOV/Px7o9ZV7EuET62VG456Hua6c8k6AliGv9sY4Mh0W9M83bXpK5MOXkKE06c0KabUY0xzF87nwc/eBDb2AzJHsL9597/pbuPJBKLUPR5Ecs/W87yHfHaj32leFMYmTeSM7ufyeDswayrWMe/S//Ne6Xvsbthd7Nxc5JyGJk3klH5oxiZN/KwNUiHUxmq5LlPnmPB+gWJZWT6M5nafyqXnXzZQcNE0wHno4qPWPP5Gj6qWMvO2tKDzj8nKScRTAozC+mX2Y/eab2b1fLUhGt4ZcsrLNywkA/LP0wM7xrsysV9L+bbfb9Nr7ReR1yXcCzMrtpdiZDy6Z5PeX3r680OeD6Xj9H5ozn/hPM5p8c5x73msK32NOxh/e71rK9Yn2g3nXTsK+gJ0j2lO91TutMjtUeiu6m/PWqvjDFUNFSwpXILW6q2fNGu2kJJVckhg+qg7EHxy3zZAynMKMTr9rK9ejtvbnuTN0re4IOyD5p9hnZP6c6YXmMY03MMp+acisty8emeTxO1H2vK1zRbVk4wh7N7ns25Pc7l1NxTea/0Pf6+8e+889k7xEwMAL/bz5heY5jYdyIjuo04qmNUWV1ZIng0hY/9/08hftz45chfMqnfpDYv62AURlrg8r/fyH/2vobfyiBgZeG3MglaXQhYmQRcWQSsTIKuLvitDNxW/AzWsuI7eYi91Ntl1Jky6uwy6uxd1Npl1NplhEzlIZdpGTc+0w2v3Q13tBtWJBc7lEMk1IVI1EUkFiNi6ohSi3HVY7nrsZra7josdz24mrpDja81NA5rwLKOfLZvGR/uWFdc0WyIZkMkGxPpgh3OIhZJI2ZbuCwLt9vg8dTj9lbh8lZiearAU4lxVWK7K4m59hK19mIIk0Jf0hhAhjWANOsEPG5PfB4uC3dT22Xhaux3WTReBjHsjK5iXf1Cdkc3AeDCTd/geQxJ/Tbp3m7YtiFmDDEbwnY91ZHPqY59Tm3sc2pj5dTZ5dTZn1NvKmgwBz/r9JJBsjmBoOlF0BQQNAW4TXrjJRhwNZbJ1XjJpqnb5aKx38ICDDSGk0ZN4aVxgNmnP2YbIrYhGrOJxgwRO96uca2lIukJjKsOK5aKf/cVuMK9CXrdBH0eknxuknxugl43Aa+F21uDcVdhu/YSsfYQZg9RO0LMhqht4u+PDTFjiMbiy42/ZhOzwbYNWDEsKwaWjeWKYYhiWTGMZQNRsGIYYhgi1JhtxGho9v4F7AKC0QEEogOwQgXEbBeRxvVKvH8ug+3ZQcT3CRHfx4Q8m8CKNN8Odg5ek/VFQxY+k4mPLvjIwm35cTUGV8uyCNl7KLNeY7f7bWwrfrnPY2eRFjqfQGgUxvYQsw22aVzf2MHf86gd30CWuxpXcDvuwDZcgZ24/Ttx+Q78YI5vTDd+k0eQHriAPdYHGCvc+JqLQGQwgYaRuBpOJhprfD9sQyRmYwEetwuPy8LjsnC7LTwuF+7Gfo/bwu2Kv+52WdjGpp5tVLtXUeP5gIirrFk5fJGT8IdOwRcajIsUfG4XXrcLjxvc7igedxSXO4zLFcZyR7BcYSwrjHGFidj1hO16wqaBSKyeCPVE7QaiNBA1DcRowKYB22rAGLBMEOwkLDsJEwtCLN62G5tYNEgsEiRme/H5d+NN2oHLvwPj345x7z3oWxkgm4ArkwZTQYPZ0/ifdGh+K5WAKwOvlYTXSsZjJeEh3rhJwk0QtwlimSRcJogxFvWmjHp2Um9K4212EuPQtRA+04WgOYGA3Rt/rABfrBe27SfWuM/YxiT+v1xN29HlwnLX0uBdQ53nP9S512H22cc9pODCR5jm+1TALiA5OphgdAjuaA9sG2wTX4bLsvC6XVieKmq9K6n0LCdkfRGa/WSR7/kaBb5zSHF3IxSxqY9EqI1WUxetpN6uosGuJmRXEzFVRKghSjW2qxp8pVie5jXVAMa4MKFc7IYexBq6E2vogR3qxkOTT2fC0PZ9MrrCSAuc+9T3qTD/adG4djQZE00DK4rLuwfLdfgaAxMLYoezsMNdsUPxwBEL52DCWUDbUm7TB5nP7cLttohEbUJRO/FBCwascDyUuOuxXA3Q1LZsTLgLdrgLJpYCtGcVqWmH+RncyRvwdXkTT3JxfIhxEa3uD5bB5anE8u7B5ak74pzsSDqxhu7Y9d3j7YbumNixqfJsK8tbQbDHn3EHdmKMi3D5GIzta1zPqsZ2JZanukUBs73Z0WRitYVEawqJ1fZr3GdayYrgDm7Bk7IBd/IG3IGD10rsy0STsKPpmEgGxnjwpKzDcsXPFmMNuYQrziVaNYS2/g8dlKsBl38Xbn8prsBOXP5S3P6dWO4D73WKhboS2TucaOVpx3CfMrj8u/CkrsGT+hHuwK4vXjEuTCQdXPHAgRXBsr5cH+F2KJtYQz52Q3diDfnEQvkQ26emw4piefbi8u3G5d0d/7/27cbl3RPvb8H/eEsZY2Eimdjh7PhncTgbOxT/TG6X7WeF8aR8iidlHZ7U9fGTRcDYXqK1fYnV9Cdac3L82NHyUuMKfIY3fRXe9KLEPAHscBa4Gj/fW7jdjbHix6DG0BGr744dygNz4L1dD33vVL6lMHJoxyqMvLp2Bx+WbqcmVk5tbDc10QpqYxXURivi/bEK6mIVRE34gGktXKS4u5LqziXFk0uqOyfe7c4l2Z2Dz4p/ePs8LvweN36PC7/Xhc/twu9t7G96zRvvbjrb8XpceF1WotvT2O12HfyAH43ZNERtGiKxxsYmFG1sR2I0RGNEYgbvPmdj+5+dedxfJH+3ZTWeFdhEYoZozBCO2fGzzcYzv2gs3m46C42fnX5xhh4/W28aZvYZFj8jMCZ+Nmub+GvGxM/gbQNlkfV8XL+QXdGDB0WvFSTZ3ZUUdzapnhxS3dmkeXNI9+aQ6csnxZvxRc2GBS6Xlei2rMaaGVe8WhaazlAOLJdtGmtB9um3iNeOxfeBxtqdffqbeixofF9deBvPij1uq1l3zIR4csM9/F/Zm4fdTy1cBFzpBKwsfGTiNhl48DfWNrFPm/2GxbstLNyWB5flxsKDhRvLeLAsN5ZxAx4s4wI8YNxkeHPJD56Iz+vB63Lh9cT3P4/Lhc8TL7/XHe92u1zN3zN73/evsbtxP6gK72VH3Rb2hHexN/Q5eyJlVIY/Z2+4jL3hzwnZBz8InZA8iHNzL2NAxunx/XOfGjZ347ZtWtcv3ucv/m887qYyW81qK6K2IRSN/6+E9v2fido0RKKU1paytXoj22o3URupYXDG1+idOgCfx73fNnUdsDyI11DFa2Xi/zdN/TG7cVhjjU387PiLGjj3PrVxLgs+r9/GB7uXsrp8KSW1Gw65n3gsH15XAK8VwGP58VgB3JYfnxXA7w7idycRcCcRcAcJeJJI8iQRbGwne5NJ9ibjdlk02DXUx6ppiNZQF6umNlpFfbSGmkgVtdFqaiJVjU0NucE8eqedREFKP3ok9yPH3xsXQRoiscb3Md4ONbab3pd9/7+aarZM4z7TEKujOlpGlBqi1BKljhh1RExTU0vY1BGxawmZWsJ2LTETJcObS5avO1m+HmT6utPF151Mbz7exvuymv7f4zV5JD77vqj1aNyn3PF20zCXy0p8nkX3azdty5htCMUilNSsI2Ii9E4ehN8TaPxfdDXuq43dLprVGkftLz5rm322xgwN0QY+qX6Pj6peZ1t9EYbmJyZBdwrJnnRSvOmkeTNI96eT7s8k059BZiCTgrQT6JNWSNAb3O+za//Prfiw1ICHgLd9b1tQGGknxhiqwlXsqttFWV0ZbstNj9Qe5CXn4XHpMS3H0trytSzdvpSMQAZ5yXnxJiWPNF/HvYl5f8YYnv74ad7a9haZgUy6JXVL3CScmxRvZwezvxL7WnW4mp21OymtLWVn7U52N+xmRN4ITs051emifWlsr95OeX05QU8wHia8QYKeIAF3oFN/PVTg87rP2Vq1lQx/BhmBePBozTcdnaIwIiIiIo5q6fHbdRzLJCIiInIAhRERERFxlMKIiIiIOEphRERERBylMCIiIiKOUhgRERERRymMiIiIiKMURkRERMRRCiMiIiLiKIURERERcZTCiIiIiDhKYUREREQcpTAiIiIijlIYEREREUcpjIiIiIijFEZERETEUQojIiIi4iiFEREREXGUwoiIiIg4SmFEREREHNXqMLJ06VImTJhAfn4+lmXx0ksvHXGaUCjErbfeSkFBAX6/nxNPPJEnnniiLeUVERGRTsbT2glqa2sZOnQoV1xxBZdcckmLprn00kvZtWsXjz/+OH379qWsrIxoNNrqwoqIiEjn0+owMn78eMaPH9/i8V955RXefvttNm/eTFZWFgAnnHBCaxcrIiIindQxv2fk5ZdfZvjw4dx77710796dwsJCfv7zn1NfX3/IaUKhEFVVVc0aERER6ZxaXTPSWps3b+add94hEAiwcOFCysvL+fGPf8zu3bsPed/InDlzuPPOO4910URERORL4JjXjNi2jWVZLFiwgDPOOIOLLrqI+++/nyeffPKQtSOzZ8+msrIy0Wzbtu1YF1NEREQccsxrRvLy8ujevTvp6emJYf3798cYw/bt2+nXr98B0/j9fvx+/7EumoiIiHwJHPOakTPPPJMdO3ZQU1OTGPbpp5/icrno0aPHsV68iIiIfMm1OozU1NRQVFREUVERAMXFxRQVFVFSUgLEL7FMmzYtMf6UKVPo0qULV1xxBevWrWPp0qXceOONXHnllQSDwfZZCxEREemwWh1G3n//fU499VROPfVUAGbNmsWpp57KbbfdBkBpaWkimACkpKSwZMkS9u7dy/Dhw5k6dSoTJkzgoYceaqdVEBERkY7MMsYYpwtxJFVVVaSnp1NZWUlaWprTxREREZEWaOnxW79NIyIiIo5SGBERERFHKYyIiIiIoxRGRERExFEKIyIiIuIohRERERFxlMKIiIiIOEphRERERBylMCIiIiKOUhgRERERRymMiIiIiKMURkRERMRRCiMiIiLiKIURERERcZTCiIiIiDhKYUREREQcpTAiIiIijlIYEREREUcpjIiIiIijFEZERETEUQojIiIi4iiFEREREXGUwoiIiIg4SmFEREREHKUwIiIiIo5SGBERERFHKYyIiIiIoxRGRERExFEKIyIiIuIohRERERFxlMKIiIiIOKrVYWTp0qVMmDCB/Px8LMvipZdeavG0y5cvx+PxcMopp7R2sSIiItJJtTqM1NbWMnToUObOnduq6SorK5k2bRpjx45t7SJFRESkE/O0doLx48czfvz4Vi/o6quvZsqUKbjd7lbVpoiIiEjndlzuGZk/fz6bNm3i9ttvPx6LExERkQ6k1TUjrbVhwwZuueUWli1bhsfTssWFQiFCoVCiv6qq6lgVT0RERBx2TGtGYrEYU6ZM4c4776SwsLDF082ZM4f09PRE07Nnz2NYShEREXGSZYwxbZ7Ysli4cCETJ0486Ot79+4lMzMTt9udGGbbNsYY3G43r732GmPGjDlguoPVjPTs2ZPKykrS0tLaWlwRERE5jqqqqkhPTz/i8fuYXqZJS0tjzZo1zYbNmzePN954g7/97W/07t37oNP5/X78fv+xLJqIiIh8SbQ6jNTU1LBx48ZEf3FxMUVFRWRlZdGrVy9mz57NZ599xlNPPYXL5WLQoEHNps/JySEQCBwwXERERL6aWh1G3n//fc4777xE/6xZswCYPn06Tz75JKWlpZSUlLRfCUVERKRTO6p7Ro6Xll5zEhERkS+Plh6/9ds0IiIi4iiFEREREXGUwoiIiIg4SmFEREREHKUwIiIiIo5SGBERERFHKYyIiIiIoxRGRERExFEKIyIiIuIohRERERFxlMKIiIiIOEphRERERBylMCIiIiKOUhgRERERRymMiIiIiKMURkRERMRRCiMiIiLiKIURERERcZTCiIiIiDhKYUREREQcpTAiIiIijlIYEREREUcpjIiIiIijFEZERETEUQojIiIi4iiFEREREXGUwoiIiIg4SmFEREREHKUwIiIiIo5SGBERERFHKYyIiIiIoxRGRERExFGtDiNLly5lwoQJ5OfnY1kWL7300mHHf/HFFzn//PPp2rUraWlpjBo1ildffbWt5RUREZFOptVhpLa2lqFDhzJ37twWjb906VLOP/98Fi9ezKpVqzjvvPOYMGECq1evbnVhRUREpPOxjDGmzRNbFgsXLmTixImtmm7gwIFMnjyZ2267rUXjV1VVkZ6eTmVlJWlpaW0oqYiIiBxvLT1+e45jmQCwbZvq6mqysrIOOU4oFCIUCiX6q6qqjkfRRERExAHH/QbW++67j9raWi699NJDjjNnzhzS09MTTc+ePY9jCUVEROR4Oq5h5JlnnuGOO+7gueeeIycn55DjzZ49m8rKykSzbdu241hKEREROZ6O22Wa5557jh/84Ac8//zzfP3rXz/suH6/H7/ff5xKJiIiBxOLxYhEIk4XQ77EvF4vbrf7qOdzXMLIM888w5VXXskzzzzDN77xjeOxSBERaSNjDDt37mTv3r1OF0U6gIyMDLp164ZlWW2eR6vDSE1NDRs3bkz0FxcXU1RURFZWFr169WL27Nl89tlnPPXUU0A8iEybNo0HH3yQkSNHsnPnTgCCwSDp6eltLriIiBwbTUEkJyeHpKSkozrISOdljKGuro6ysjIA8vLy2jyvVoeR999/n/POOy/RP2vWLACmT5/Ok08+SWlpKSUlJYnXH3nkEaLRKNdccw3XXHNNYnjT+CIi8uURi8USQaRLly5OF0e+5ILBIABlZWXk5OS0+ZJNq8PIueeey+EeTbJ/wHjrrbdauwgREXFI0z0iSUlJDpdEOoqmfSUSibQ5jOi3aURE5AC6NCMt1R77isKIiIiIOEphREREOoVzzz2XG264weliSBsojIiIiIijFEZERETEUQojIiLS6ezZs4dp06aRmZlJUlIS48ePZ8OGDYnXt27dyoQJE8jMzCQ5OZmBAweyePHixLRTp06la9euBINB+vXrx/z5851ala+E4/6rvSIi0rEYY6iPxBxZdtDrbtO3NWbMmMGGDRt4+eWXSUtL4+abb+aiiy5i3bp1eL1errnmGsLhMEuXLiU5OZl169aRkpICwC9/+UvWrVvHP//5T7Kzs9m4cSP19fXtvWqyD4URERE5rPpIjAG3verIstf96gKSfK07VDWFkOXLlzN69GgAFixYQM+ePXnppZf47ne/S0lJCZdccgmDBw8GoE+fPonpS0pKOPXUUxk+fDgAJ5xwQvusjBySLtOIiEinsn79ejweDyNGjEgM69KlCyeddBLr168H4LrrruOuu+7izDPP5Pbbb+fDDz9MjPujH/2IZ599llNOOYWbbrqJFStWHPd1+KpRzYiIiBxW0Otm3a8ucGzZrXWop4QbYxKXfK666iouuOACFi1axGuvvcacOXO47777+MlPfsL48ePZunUrixYt4vXXX2fs2LFcc801/Pa3vz2qdZFDU82IiIgclmVZJPk8jjRtuV9kwIABRKNR3nvvvcSwiooKPv30U/r3758Y1rNnT2bOnMmLL77Iz372Mx577LHEa127dmXGjBn85S9/4YEHHuDRRx89ujdRDks1IyIi0qn069ePiy++mP/3//4fjzzyCKmpqdxyyy10796diy++GIAbbriB8ePHU1hYyJ49e3jjjTcSQeW2225j2LBhDBw4kFAoxD/+8Y9mIUban2pGRESk05k/fz7Dhg3jm9/8JqNGjcIYw+LFi/F6vUD814mvueYa+vfvz4UXXshJJ53EvHnzAPD5fMyePZshQ4Zw9tln43a7efbZZ51cnU7PMof7Cd4viaqqKtLT06msrCQtLc3p4oiIdFoNDQ0UFxfTu3dvAoGA08WRDuBw+0xLj9+qGRERERFHKYyIiIiIoxRGRERExFEKIyIiIuIohRERERFxlMKIiIiIOEphRERERBylMCIiIiKOUhgRERERRymMiIiIiKMURkRERMRRCiMiIiLHSCQScboIHYLCiIiIdBqvvPIKX/va18jIyKBLly5885vfZNOmTYnXt2/fzmWXXUZWVhbJyckMHz6c9957L/H6yy+/zPDhwwkEAmRnZzNp0qTEa5Zl8dJLLzVbXkZGBk8++SQAW7ZswbIs/vrXv3LuuecSCAT4y1/+QkVFBd/73vfo0aMHSUlJDB48mGeeeabZfGzb5p577qFv3774/X569erFr3/9awDGjBnDtdde22z8iooK/H4/b7zxRnu8bY7zOF0AERH5kjMGInXOLNubBJbV4tFra2uZNWsWgwcPpra2lttuu41vf/vbFBUVUVdXxznnnEP37t15+eWX6datGx988AG2bQOwaNEiJk2axK233sqf//xnwuEwixYtanWRb775Zu677z7mz5+P3++noaGBYcOGcfPNN5OWlsaiRYu4/PLL6dOnDyNGjABg9uzZPPbYY/zud7/ja1/7GqWlpXz88ccAXHXVVVx77bXcd999+P1+ABYsWEB+fj7nnXdeq8v3ZWQZY4zThTiSlv4EsYiIHJ2D/hx8uBZ+k+9Mgf5rB/iS2zz5559/Tk5ODmvWrGHFihX8/Oc/Z8uWLWRlZR0w7ujRo+nTpw9/+ctfDjovy7JYuHAhEydOTAzLyMjggQceYMaMGWzZsoXevXvzwAMPcP311x+2XN/4xjfo378/v/3tb6murqZr167MnTuXq6666oBxQ6EQ+fn5PPzww1x66aUAnHrqqUycOJHbb7+9Fe/GsXHQfaZRS4/fukwjIiKdxqZNm5gyZQp9+vQhLS2N3r17A1BSUkJRURGnnnrqQYMIQFFREWPHjj3qMgwfPrxZfywW49e//jVDhgyhS5cupKSk8Nprr1FSUgLA+vXrCYVCh1y23+/n+9//Pk888USinP/5z3+YMWPGUZf1y0KXaURE5PC8SfEaCqeW3QoTJkygZ8+ePPbYY+Tn52PbNoMGDSIcDhMMBg877ZFetyyL/S8mHOwG1eTk5jU59913H7/73e944IEHGDx4MMnJydxwww2Ew+EWLRfil2pOOeUUtm/fzhNPPMHYsWMpKCg44nQdRatrRpYuXcqECRPIz88/6M08B/P2228zbNgwAoEAffr04Q9/+ENbyioiIk6wrPilEieaVtwvUlFRwfr16/nFL37B2LFj6d+/P3v27Em8PmTIEIqKiti9e/dBpx8yZAj/+te/Djn/rl27UlpamujfsGEDdXVHvpdm2bJlXHzxxXz/+99n6NCh9OnThw0bNiRe79evH8Fg8LDLHjx4MMOHD+exxx7j6aef5sorrzzicjuSVoeR2tpahg4dyty5c1s0fnFxMRdddBFnnXUWq1ev5r/+67+47rrreOGFF1pdWBERkUPJzMykS5cuPProo2zcuJE33niDWbNmJV7/3ve+R7du3Zg4cSLLly9n8+bNvPDCC/z73/8G4Pbbb+eZZ57h9ttvZ/369axZs4Z77703Mf2YMWOYO3cuH3zwAe+//z4zZ87E6/UesVx9+/ZlyZIlrFixgvXr13P11Vezc+fOxOuBQICbb76Zm266iaeeeopNmzbx7rvv8vjjjzebz1VXXcXdd99NLBbj29/+9tG+XV8u5igAZuHChYcd56abbjInn3xys2FXX321GTlyZIuXU1lZaQBTWVnZlmKKiEgL1dfXm3Xr1pn6+nqni9ImS5YsMf379zd+v98MGTLEvPXWW82OVVu2bDGXXHKJSUtLM0lJSWb48OHmvffeS0z/wgsvmFNOOcX4fD6TnZ1tJk2alHjts88+M+PGjTPJycmmX79+ZvHixSY9Pd3Mnz/fGGNMcXGxAczq1aublamiosJcfPHFJiUlxeTk5Jhf/OIXZtq0aebiiy9OjBOLxcxdd91lCgoKjNfrNb169TK/+c1vms2nurraJCUlmR//+Mft+p4drcPtMy09fh/Vt2kOdmfx/s4++2xOPfVUHnzwwcSwhQsXcumll1JXV9eiVKlv04iIHB+H+2aEOGvbtm2ccMIJrFy5ktNOO83p4iS0x7dpjvkNrDt37iQ3N7fZsNzcXKLRKOXl5eTl5R0wTSgUIhQKJfqrqqqOdTFFRES+lCKRCKWlpdxyyy2MHDnySxVE2stx+Wqvtd8NSE2VMfsPbzJnzhzS09MTTc+ePY95GUVERL6Mli9fTkFBAatWreq0XwA55jUj3bp1a3ajDkBZWRkej4cuXbocdJrZs2c3u+moqqpKgURERL6Szj333AO+UtzZHPMwMmrUKP73f/+32bDXXnuN4cOHH/J+Eb/fn3jkrYiIiHRurb5MU1NTQ1FREUVFRUD8q7tFRUWJJ8nNnj2badOmJcafOXMmW7duZdasWaxfv54nnniCxx9/nJ///OftswYiIiLSobW6ZuT9999v9sM8TZdTpk+fzpNPPklpaWkimAD07t2bxYsX89Of/pT/+Z//IT8/n4ceeohLLrmkHYovIiIiHV2rw8iRrl01/ZTyvs455xw++OCD1i5KREREvgL0Q3kiIiLiKIURERERcZTCiIiICHDCCSfwwAMPtGjclv5QrLSMwoiIiIg4SmFEREREHKUwIiIiHd4jjzxC9+7dsW272fBvfetbTJ8+nU2bNnHxxReTm5tLSkoKp59+Oq+//nq7LX/NmjWMGTOGYDBIly5d+OEPf0hNTU3i9bfeeoszzjiD5ORkMjIyOPPMM9m6dSsA//nPfzjvvPNITU0lLS2NYcOG8f7777db2ToChRERETksYwx1kTpHmpY+Bv273/0u5eXlvPnmm4lhe/bs4dVXX2Xq1KnU1NRw0UUX8frrr7N69WouuOACJkyY0Oy5WG1VV1fHhRdeSGZmJitXruT555/n9ddf59prrwUgGo0yceJEzjnnHD788EP+/e9/88Mf/jDx+2xTp06lR48erFy5klWrVnHLLbe06BftO5Nj/jh4ERHp2Oqj9Yx4eoQjy35vynskeZOOOF5WVhYXXnghTz/9NGPHjgXg+eefJysri7Fjx+J2uxk6dGhi/LvuuouFCxfy8ssvJ0JDWy1YsID6+nqeeuopkpOTAZg7dy4TJkzgnnvuwev1UllZyTe/+U1OPPFEAPr375+YvqSkhBtvvJGTTz4ZgH79+h1VeToi1YyIiEinMHXqVF544QVCoRAQDwmXXXYZbreb2tpabrrpJgYMGEBGRgYpKSl8/PHH7VIzsn79eoYOHZoIIgBnnnkmtm3zySefkJWVxYwZMxK1MQ8++CClpaWJcWfNmsVVV13F17/+de6++242bdp01GXqaFQzIiIihxX0BHlvynuOLbulJkyYgG3bLFq0iNNPP51ly5Zx//33A3DjjTfy6quv8tvf/pa+ffsSDAb5zne+QzgcPuoyGmMSl1z21zR8/vz5XHfddbzyyis899xz/OIXv2DJkiWMHDmSO+64gylTprBo0SL++c9/cvvtt/Pss8/y7W9/+6jL1lEojIiIyGFZltWiSyVOCwaDTJo0iQULFrBx40YKCwsZNmwYAMuWLWPGjBmJA3xNTQ1btmxpl+UOGDCAP/3pT9TW1iZqR5YvX47L5aKwsDAx3qmnnsqpp57K7NmzGTVqFE8//TQjR44EoLCwkMLCQn7605/yve99j/nz53+lwogu04iISKcxdepUFi1axBNPPMH3v//9xPC+ffvy4osvUlRUxH/+8x+mTJlywDdvjmaZgUCA6dOn89FHH/Hmm2/yk5/8hMsvv5zc3FyKi4uZPXs2//73v9m6dSuvvfYan376Kf3796e+vp5rr72Wt956i61bt7J8+XJWrlzZ7J6SrwLVjIiISKcxZswYsrKy+OSTT5gyZUpi+O9+9zuuvPJKRo8eTXZ2NjfffDNVVVXtssykpCReffVVrr/+ek4//XSSkpK45JJLEpeIkpKS+Pjjj/nTn/5ERUUFeXl5XHvttVx99dVEo1EqKiqYNm0au3btIjs7m0mTJnHnnXe2S9k6Csu09HtTDqqqqiI9PZ3KykrS0tKcLo6ISKfV0NBAcXExvXv3JhAIOF0c6QAOt8+09PityzQiIiLiKIURERGRfSxYsICUlJSDNgMHDnS6eJ2S7hkRERHZx7e+9S1GjDj4Q96+ak9GPV4URkRERPaRmppKamqq08X4StFlGhEREXGUwoiIiIg4SmFEREREHKUwIiIiIo5SGBERERFHKYyIiIgAJ5xwAg888IDTxfhKUhgRERERRymMiIiIdHCxWKzdfoXYCQojIiLS4T3yyCN07979gAPyt771LaZPn86mTZu4+OKLyc3NJSUlhdNPP53XX3+9zcu7//77GTx4MMnJyfTs2ZMf//jH1NTUNBtn+fLlnHPOOSQlJZGZmckFF1zAnj17ALBtm3vuuYe+ffvi9/vp1asXv/71rwF46623sCyLvXv3JuZVVFSEZVls2bIFgCeffJKMjAz+8Y9/MGDAAPx+P1u3bmXlypWcf/75ZGdnk56ezjnnnMMHH3zQrFx79+7lhz/8Ibm5uQQCAQYNGsQ//vEPamtrSUtL429/+1uz8f/3f/+X5ORkqqur2/x+HYnCiIiIHJYxBruuzpGmpT8s/93vfpfy8nLefPPNxLA9e/bw6quvMnXqVGpqarjooot4/fXXWb16NRdccAETJkygpKSkTe+Jy+XioYce4qOPPuJPf/oTb7zxBjfddFPi9aKiIsaOHcvAgQP597//zTvvvMOECROIxWIAzJ49m3vuuYdf/vKXrFu3jqeffprc3NxWlaGuro45c+bwxz/+kbVr15KTk0N1dTXTp09n2bJlvPvuu/Tr14+LLrooESRs22b8+PGsWLGCv/zlL6xbt467774bt9tNcnIyl112GfPnz2+2nPnz5/Od73znmD6VVo+DFxGRwzL19Xxy2jBHln3SB6uwkpKOOF5WVhYXXnghTz/9NGPHjgXg+eefJysri7Fjx+J2uxk6dGhi/LvuuouFCxfy8ssvc+2117a6XDfccEOiu3fv3vz3f/83P/rRj5g3bx4A9957L8OHD0/0A4kf2auurubBBx9k7ty5TJ8+HYATTzyRr33ta60qQyQSYd68ec3Wa8yYMc3GeeSRR8jMzOTtt9/mm9/8Jq+//jr/93//x/r16yksLASgT58+ifGvuuoqRo8ezY4dO8jPz6e8vJx//OMfLFmypFVlay3VjIiISKcwdepUXnjhBUKhEBD/9d3LLrsMt9tNbW0tN910EwMGDCAjI4OUlBQ+/vjjNteMvPnmm5x//vl0796d1NRUpk2bRkVFBbW1tcAXNSMHs379ekKh0CFfbymfz8eQIUOaDSsrK2PmzJkUFhaSnp5Oeno6NTU1ifUsKiqiR48eiSCyvzPOOIOBAwfy1FNPAfDnP/+ZXr16cfbZZx9VWY9ENSMiInJYVjDISR+scmzZLTVhwgRs22bRokWcfvrpLFu2jPvvvx+AG2+8kVdffZXf/va39O3bl2AwyHe+8x3C4XCry7R161YuuugiZs6cyX//93+TlZXFO++8ww9+8AMikQgAwcOU+3CvQfwSENDsElXTfPefj2VZzYbNmDGDzz//nAceeICCggL8fj+jRo1KrOeRlg3x2pG5c+dyyy23MH/+fK644ooDltPeFEZEROSwLMtq0aUSpwWDQSZNmsSCBQvYuHEjhYWFDBsWv7y0bNkyZsyYwbe//W0AampqEjeDttb7779PNBrlvvvuSwSHv/71r83GGTJkCP/617+48847D5i+X79+BINB/vWvf3HVVVcd8HrXrl0BKC0tJTMzE4jXaLTEsmXLmDdvHhdddBEA27Zto7y8vFm5tm/fzqeffnrI2pHvf//73HTTTTz00EOsXbs2cSnpWGrTZZp58+bRu3dvAoEAw4YNY9myZYcdf8GCBQwdOpSkpCTy8vK44oorqKioaFOBRUREDmXq1KksWrSIJ554gu9///uJ4X379uXFF1+kqKiI//znP0yZMqXNX4U98cQTiUaj/P73v2fz5s38+c9/5g9/+EOzcWbPns3KlSv58Y9/zIcffsjHH3/Mww8/THl5OYFAgJtvvpmbbrqJp556ik2bNvHuu+/y+OOPJ8ras2dP7rjjDj799FMWLVrEfffd16Ky9e3blz//+c+sX7+e9957j6lTpzarDTnnnHM4++yzueSSS1iyZAnFxcX885//5JVXXkmMk5mZyaRJk7jxxhsZN24cPXr0aNP71CqmlZ599lnj9XrNY489ZtatW2euv/56k5ycbLZu3XrQ8ZctW2ZcLpd58MEHzebNm82yZcvMwIEDzcSJE1u8zMrKSgOYysrK1hZXRERaob6+3qxbt87U19c7XZQ2iUajJi8vzwBm06ZNieHFxcXmvPPOM8Fg0PTs2dPMnTvXnHPOOeb6669PjFNQUGB+97vftWg5999/v8nLyzPBYNBccMEF5qmnnjKA2bNnT2Kct956y4wePdr4/X6TkZFhLrjggsTrsVjM3HXXXaagoMB4vV7Tq1cv85vf/CYx7TvvvGMGDx5sAoGAOeuss8zzzz9vAFNcXGyMMWb+/PkmPT39gHJ98MEHZvjw4cbv95t+/fqZ559//oD1qqioMFdccYXp0qWLCQQCZtCgQeYf//hHs/n861//MoD561//esT34nD7TEuP35YxLfzeVKMRI0Zw2mmn8fDDDyeG9e/fn4kTJzJnzpwDxv/tb3/Lww8/zKZNmxLDfv/733Pvvfeybdu2Fi2zqqqK9PR0KisrSUtLa01xRUSkFRoaGiguLk7UfstX04IFC7j++uvZsWMHPp/vsOMebp9p6fG7VZdpwuEwq1atYty4cc2Gjxs3jhUrVhx0mtGjR7N9+3YWL16MMYZdu3bxt7/9jW984xuHXE4oFKKqqqpZIyIiIsdWXV0da9euZc6cOVx99dVHDCLtpVVhpLy8nFgsdsCDWXJzc9m5c+dBpxk9ejQLFixg8uTJ+Hw+unXrRkZGBr///e8PuZw5c+YkvpKUnp5Oz549W1NMERGRNluwYAEpKSkHbZqeFdJZ3XvvvZxyyink5uYye/bs47bcNn2bZv+v+BhjDvm1n3Xr1nHddddx2223ccEFF1BaWsqNN97IzJkzEzfr7G/27NnMmjUr0V9VVaVAIiIix8W3vvUtRowYcdDXvF7vcS7N8XXHHXdwxx13HPfltiqMZGdn43a7D6gFKSsrO+RjbOfMmcOZZ57JjTfeCMS/VpScnMxZZ53FXXfdRV5e3gHT+P1+/H5/a4omIiLSLlJTU4/po8/lQK26TOPz+Rg2bNgBj4VdsmQJo0ePPug0dXV1ie9hN3G73QAt/s0BERER6bxa/ZyRWbNm8cc//pEnnniC9evX89Of/pSSkhJmzpwJxC+xTJs2LTH+hAkTePHFF3n44YfZvHkzy5cv57rrruOMM84gPz+//dZERETajU4WpaXaY19p9T0jkydPpqKigl/96leUlpYyaNAgFi9eTEFBARB/Yty+z/qfMWMG1dXVzJ07l5/97GdkZGQwZswY7rnnnqMuvIiItK+meyLq6upa9Ohwkbq6OuDo7qdp9XNGnKDnjIiIHD+lpaXs3buXnJwckpKSjvnvkkjHZIyhrq6OsrIyMjIyDnoPaEuP3/ptGhERaaZbt25A/MsJIkeSkZGR2GfaSmFERESasSyLvLw8cnJyDvprsSJNvF5v4kspR0NhREREDsrtdrfLgUbkSNr0q70iIiIi7UVhRERERBylMCIiIiKOUhgRERERRymMiIiIiKMURkRERMRRCiMiIiLiKIURERERcZTCiIiIiDhKYUREREQcpTAiIiIijlIYEREREUcpjIiIiIijFEZERETEUQojIiIi4iiFEREREXGUwoiIiIg4SmFEREREHKUwIiIiIo5SGBERERFHKYyIiIiIoxRGRERExFEKIyIiIuIohRERERFxlMKIiIiIOEphRERERBylMCIiIiKOUhgRERERRymMiIiIiKPaFEbmzZtH7969CQQCDBs2jGXLlh12/FAoxK233kpBQQF+v58TTzyRJ554ok0FFhERkc7F09oJnnvuOW644QbmzZvHmWeeySOPPML48eNZt24dvXr1Oug0l156Kbt27eLxxx+nb9++lJWVEY1Gj7rwIiIi0vFZxhjTmglGjBjBaaedxsMPP5wY1r9/fyZOnMicOXMOGP+VV17hsssuY/PmzWRlZbWpkFVVVaSnp1NZWUlaWlqb5iEiIiLHV0uP3626TBMOh1m1ahXjxo1rNnzcuHGsWLHioNO8/PLLDB8+nHvvvZfu3btTWFjIz3/+c+rr6w+5nFAoRFVVVbNGREREOqdWXaYpLy8nFouRm5vbbHhubi47d+486DSbN2/mnXfeIRAIsHDhQsrLy/nxj3/M7t27D3nfyJw5c7jzzjtbUzQRERHpoNp0A6tlWc36jTEHDGti2zaWZbFgwQLOOOMMLrroIu6//36efPLJQ9aOzJ49m8rKykSzbdu2thRTREREOoBW1YxkZ2fjdrsPqAUpKys7oLakSV5eHt27dyc9PT0xrH///hhj2L59O/369TtgGr/fj9/vb03RREREpINqVc2Iz+dj2LBhLFmypNnwJUuWMHr06INOc+aZZ7Jjxw5qamoSwz799FNcLhc9evRoQ5FFRESkM2n1ZZpZs2bxxz/+kSeeeIL169fz05/+lJKSEmbOnAnEL7FMmzYtMf6UKVPo0qULV1xxBevWrWPp0qXceOONXHnllQSDwfZbExEREemQWv2ckcmTJ1NRUcGvfvUrSktLGTRoEIsXL6agoACA0tJSSkpKEuOnpKSwZMkSfvKTnzB8+HC6dOnCpZdeyl133dV+ayEiIiIdVqufM+IEPWdERESk4zkmzxkRERERaW8KIyIiIuIohRERERFxlMKIiIiIOEphRERERBylMCIiIiKOUhgRERERRymMiIiIiKMURkRERMRRCiMiIiLiKIURERERcZTCiIiIiDhKYUREREQcpTAiIiIijlIYEREREUcpjIiIiIijFEZERETEUQojIiIi4iiFEREREXGUwoiIiIg4SmFEREREHKUwIiIiIo5SGBERERFHKYyIiIiIoxRGRERExFEKIyIiIuIohRERERFxlMKIiIiIOEphRERERBylMCIiIiKOUhgRERERR7UpjMybN4/evXsTCAQYNmwYy5Yta9F0y5cvx+PxcMopp7RlsSIiItIJtTqMPPfcc9xwww3ceuutrF69mrPOOovx48dTUlJy2OkqKyuZNm0aY8eObXNhRUREpPOxjDGmNROMGDGC0047jYcffjgxrH///kycOJE5c+YccrrLLruMfv364Xa7eemllygqKmrxMquqqkhPT6eyspK0tLTWFFdEREQc0tLjd6tqRsLhMKtWrWLcuHHNho8bN44VK1Yccrr58+ezadMmbr/99hYtJxQKUVVV1awRERGRzqlVYaS8vJxYLEZubm6z4bm5uezcufOg02zYsIFbbrmFBQsW4PF4WrScOXPmkJ6enmh69uzZmmKKiIhIB9KmG1gty2rWb4w5YBhALBZjypQp3HnnnRQWFrZ4/rNnz6aysjLRbNu2rS3FFBERkQ6gZVUVjbKzs3G73QfUgpSVlR1QWwJQXV3N+++/z+rVq7n22msBsG0bYwwej4fXXnuNMWPGHDCd3+/H7/e3pmgiIiLSQbWqZsTn8zFs2DCWLFnSbPiSJUsYPXr0AeOnpaWxZs0aioqKEs3MmTM56aSTKCoqYsSIEUdXehEREenwWlUzAjBr1iwuv/xyhg8fzqhRo3j00UcpKSlh5syZQPwSy2effcZTTz2Fy+Vi0KBBzabPyckhEAgcMFxERES+mlodRiZPnkxFRQW/+tWvKC0tZdCgQSxevJiCggIASktLj/jMEREREZEmrX7OiBP0nBEREZGO55g8Z0RERESkvSmMiIiIiKMURkRERMRRCiMiIiLiKIURERERcZTCiIiIiDhKYUREREQcpTAiIiIijlIYEREREUcpjIiIiIijFEZERETEUQojIiIi4iiFEREREXGUwoiIiIg4SmFEREREHKUwIiIiIo5SGBERERFHKYyIiIiIoxRGRERExFEKIyIiIuIohRERERFxlMKIiIiIOEphRERERBylMCIiIiKOUhgRERERRymMiIiIiKMURkRERMRRCiMiIiLiKIURERERcZTCiIiIiDhKYUREREQcpTAiIiIijmpTGJk3bx69e/cmEAgwbNgwli1bdshxX3zxRc4//3y6du1KWloao0aN4tVXX21zgUVERKRzaXUYee6557jhhhu49dZbWb16NWeddRbjx4+npKTkoOMvXbqU888/n8WLF7Nq1SrOO+88JkyYwOrVq4+68CIiItLxWcYY05oJRowYwWmnncbDDz+cGNa/f38mTpzInDlzWjSPgQMHMnnyZG677bYWjV9VVUV6ejqVlZWkpaW1prjyJWFCDcQ+/4xY2TaIRXElJWMlJcfbwWQsjw9cbnB5wOVtbHeAq4jGgB2FWATsCMSije1IfLgda2xHD9HfOMzY4PGDJ3Bg2xuMd7v94Pa0/zpEQ1BbDrWfx9sYCKSDPy3eDqTHy2BZX6x2NIpdX49paMDYJl5+296v2258f5q6wfJ58WRm4kpLw3Jo+xpjMA0N4HZjeb1Y+6yXHAO2DaEqiIXj+5LHf9SzNMZAOISprsBUl0G4Bld6DlZKFgTS2mUZbSmTqa/HDoVaNZ3L78cKBBz7f9iXMabd/x9aevxu1SdbOBxm1apV3HLLLc2Gjxs3jhUrVrRoHrZtU11dTVZW1iHHCYVChPbZoFVVVa0pZovVvrGYyLZifDkZ+Lqm4k7xYkXrIVIPkbrG9r7ddfEP7n0PNvsehGJhTDRMpDJMZE+YSJWNbQUxrmRsVxBjBTGWD4MX2/ZijBsTs7CjNiYcxkQjYDd+eBv7oN0Yk+h3+X24koK4ggFcQX+88XtxBX24Al5cfg8unxuX343lcYGxMI2VYcZYYCwwTd0GY1yN/RDviDUu08bCbtYPdvwgio2JhLH37ia2dy+xqmpi1bXEahuI1YaxG6LEGgx25PA7uOW2cXkMLrfB8ph9ui2wPBjLHW/jBsvduB5uDBYYF2DFu3HFQ43lAsvdGGha+s/VFCyiNA8WjW071niwbXwLjIm/V4b4e9vYHX//LHwpUQKZEQJZEQJZYQIZUVyeVmX/L0pmIFLrJlzjJ1wTIBbzYlx+DD6M5QW8GOLvjzFNjYWxLYwNlmlahwiWHQY7HG/H4vuzZZkv3iZjYUct7JiFaWzbUQtju+PDo4DdptX4gmXhTvHjTgniSQviTk2KN2nJeNJScKcl405LwSL+f0WkIV7WaAiiDY3txu7G1+1IlPgqWcTCxPe7EMRCNrEGQ6zBJlYfw26IYWKN28ECy+vC5XXv03bj8jW1PVheN+4kH/5uafi7Z+LvnoUnNRjfxxL7mhXf3yxX43APuL2NAdu7T78HO2wT2vY5DVtKaSjeQWRXBb68bPwn5BPolYO/ZzYur3XokGtZ4AkeJLQGsG034dIKQltKaSjeTnj7TkzMxnK5wR0vr+Vyx8vldmE1ld3lwnJZjW+IReJ/IRGe92ubGNgxPOl+/F0D+LPc+NJiuOwaqN8LDZWNzV5oqIrPr4k3CQIZEMxobGcSjSbRsBtCn4cJ7awltGMvsdp6TCQC0SgmFsNE4/9/JmYOuf+5PDYur8HtA1fAhSvgwZ3kw5UUwJ2chCslGXdaGlYg2HjC44kHfJcHy+09cLu5vQDEqvZi791DrKoSu6qaWE0Ndk0dsboGYnVh7PoosVDsqP4vLI8V3/98B9sP4/uiO8mHPz8Nf34m/u6ZuJMDjZ9z7i/azbob18Hta2zHu43lIbqnhobiUho2bqdhUwn1nxbT4/57SRp5VttX4ii0KoyUl5cTi8XIzc1tNjw3N5edO3e2aB733XcftbW1XHrppYccZ86cOdx5552tKVqb7H3kN1T9pyLRb3lsfCkxfClRvI3tpm5vUgyrMbgau/HAUO0hXOMmXOMhUuMhXO0mXOsBe9+DXxioPObr8uX2xfvh8sYPeiYKZp/3ycRcxGIQO+Q8DBBpbJzS+GHdQqFKL6FKL5VbvpjcnwmBri4COR4CuV4COX5cfk/8ABANYzfUE94dIlweIbQnRnivIVTpJlztxsT2P3OKNjZHw0MrPwb2Y744flnxfmuftyne/cWB38Qs7IgLjCFW3UCsuoFw6Z6jWP5RMmDCNrFw644inkAMf0YEf0aUQHoEf0YEX1oUl7v5eNEGFw17vIT2emnY46Fhr5dwtSd+IrCP2v0K5UuN4W+cbyAzQiAjiicplqicMgaidS4aKuPzDu31xPe3qgPnfTx5k6L40qP406L40praX1RU2FEI7Y4Q2ruHUGUNDXt3EdrrIRZyH37GLWRHXdhRiNbT+LEbA+obGwf3sxYwUUMsGo0XtYW8SVH8GdH4fpIRwZ8exZcaTRyr4IsTmYY9Xhp2e+PtPd6DvucNS//eMcJIk/2rcVpatfPMM89wxx138Pe//52cnJxDjjd79mxmzZqV6K+qqqJnz55tKephBXpkE/38cyLVLiI1YKIuQntdhPZ6DxzZ7cLXJSW+YSuq4zUVh2B5PHjzsvF2y8btc2FZMSwrgkUIl2nAshuw7FpcsVosU4/lMlhuE9+BrMbP8cYz1Xh303DT7FhoGs9Y7YgL2/Zgx7zYUTe27caOuohFXfHXovGDf+LAYNF4JmwSy4rP28SHY/Y5ujQeaazGWod9+g3xsyrL5cKdHMSdloIrLRV3egbuzC64u3TFnd0Nd9d83Dk9cWXnY/kC+5Q/hl3fgGmox66rw66txtTVYNfVYtfVYepqsetrsWIhsEPxdqwhXnsVq4doA1asHqL1WNE6iNZhRWohVA2hmvhZc2u53JCUDSm5kNIVkrtCcg6k5EAwXsVseXzg8cS7vT7w+OJttw/L6wevF2yb0KZNNHy0loa1a6lf+xGxz8sJ7YbQbpvKT8JAGFz1+E88EU+3boS3biWyvbJx3zowJFheL76e3fH16o47LQnLshubGBYRLKLxxoTBRLBMKL6vmSj4UjC+VPClgjcF402KD/MmgycJ3IF41XfMxvJ6sAIBXMEkXAE/lgdcbhuXFcFyRXBZYVzUx/fdWC2WaawqMo21Z4nuxtqzfV+3o5hIhGhNA7GaBmI1IWK1IaI1IWI1YWJ1EWK1EWJ1EaJ1sfiBNVHz4NqvaT7M8nhwpwRxJQdxpwRwJ8cbV5Ifd7Ifd5IXd5IvfqYc9EAshgmHsRtCmHAEOxTGhMLY4Wi8OxzBDkUw4QjR6npCpVWEPqskUlFLtMFNdKeb2n3PwVwW/q5B/LnJ2KEoDTtqiFYfPDy7k10Est0Esl14Uw3hKgiVGxoqYsRq7fiJTrWH6u3BL2YfcOPvloIFNOyswW44eHR3+SCQZeNPj+JPC2FZ8cBqGmtC4900djfWjtLYf7CPtaaz6n1reBrPriPVFqGKCOHP64jVhonUeYjUeagt3W8WWZm4U9MIb9sWr13dnwW+rin4c5PwZ3vwZ9p40pKwktKxkjOxkjMhJQsrOQsrpQtWatdEQyD+mWLX1GBXVhKr2Im9exex3Z9j7y0ntnc3dlUlsaoq7JoaYjW18RqXxD5p9tl3992XG2ujMbgCHlxJvsZ9KglXagru1FRc6em407vgysjCndUVd1YOVlLK4S/PxiKNNUvx7sQ+GIpgh0Lxfa8h1LjvhbFDjfthKEK0spaGz/YQ+mwv0b31ife7ZscXn6uWx8KXk4S/a5BoVYiG0rqD7ysu8Ge5CGTH95dAVpTABV876D51PLQqjGRnZ+N2uw+oBSkrKzugtmR/zz33HD/4wQ94/vnn+frXv37Ycf1+P37/sb/m1+W+l+jS2G3CYcKffUakpIRwyTbC20qIbC0hvG0bkW3bMJEI4bIvLhdZgQC+Xr3wFfTC26sXvl4F+Ap64evZE0+3bljuFib9SEPjtfoyCNclqnIT909Y7ub9icYd/5Dw+OPtDnjd23K7cackQ0rysVlANByvKq7fE68urt/7Rbt+D4Sr42EjvQek94w3KTkccHrbRr5evUg977xEf2RXGQ1r4+Gk4aOPqF+3Nh5QNmwgtGFDYjxXair+Pn3wnXgi/j698fWJt709emB5jsE9I8eZBXgbm44oVlNLeOMGGj75lNCnnxL65BMaPv0Uu6qK0K46QrvqvhjZsvAVFODvfzKBk/sTGNCfwMkn4+na9ZDzj1ZU0PDxx4Q++ZTQJx/Hl7NpE3ZDhPot+9Syejz4e/fGf9JJ+AsLCZxUiP+kk/Dk5jY/OWw6wCbuYzrMvUsAvqT4pRRvMH75p4WfLdHduwlv2kRoczHhzZsIbdpMaPMmojtKie3eQ2x3vGbCnZGB/+ST8Rf2I3DSSfgLT8Lf90RcweARlnB4rqwsyMqC3r2Paj7HW9NpXmvFKisJffppfP/45JN494YNmLo6QjtqCe34or7N8nrj+8iAAQQGDiQwcAD+wkJcx+E421JtuoF12LBhzJs3LzFswIABXHzxxYe8gfWZZ57hyiuv5JlnnmHixImtLqTTN7CaWIzorl2ES7ZhuV14CwrwdO2qG9/kqMUDykdEy8vxFZyAv09v3NnZ2rc6GGMM0Z074+FkwwasYJBA//gHvrsdwrYJhwkVFxP65BMA/CedhK93b1w+31HP+1iza2sJFW/Brq7Cd+KJ+uw8hoxtE9m+Pb4fbtyEu0sWwYED8ffti+XQvtLS43erw8hzzz3H5Zdfzh/+8AdGjRrFo48+ymOPPcbatWspKChg9uzZfPbZZzz11FNAPIhMmzaNBx98kEmTJiXmEwwGSU9Pb9eVERERkS+PY/JtGoDJkydTUVHBr371K0pLSxk0aBCLFy+moKAAgNLS0mbPHHnkkUeIRqNcc801XHPNNYnh06dP58knn2zt4kVERKSTaXXNiBNUMyIiItLxtPT47fxTVkREROQrTWFEREREHKUwIiIiIo5SGBERERFHKYyIiIiIoxRGRERExFEKIyIiIuIohRERERFxlMKIiIiIOEphRERERBylMCIiIiKOavUP5Tmh6edzqqqqHC6JiIiItFTTcftIP4PXIcJIdXU1AD179nS4JCIiItJa1dXVpKenH/L1DvGrvbZts2PHDlJTU7Esq93mW1VVRc+ePdm2bVun/jVgrWfnovXsPL4K6whaz86mNetpjKG6upr8/HxcrkPfGdIhakZcLhc9evQ4ZvNPS0vr1DtOE61n56L17Dy+CusIWs/OpqXrebgakSa6gVVEREQcpTAiIiIijvpKhxG/38/tt9+O3+93uijHlNazc9F6dh5fhXUErWdncyzWs0PcwCoiIiKd11e6ZkREREScpzAiIiIijlIYEREREUcpjIiIiIijvtJhZN68efTu3ZtAIMCwYcNYtmyZ00VqV3fccQeWZTVrunXr5nSxjtrSpUuZMGEC+fn5WJbFSy+91Ox1Ywx33HEH+fn5BINBzj33XNauXetMYY/CkdZzxowZB2zfkSNHOlPYNpozZw6nn346qamp5OTkMHHiRD755JNm43SG7dmS9ezo2/Phhx9myJAhiQdhjRo1in/+85+J1zvDdoQjr2dH346HMmfOHCzL4oYbbkgMa89t+pUNI8899xw33HADt956K6tXr+ass85i/PjxlJSUOF20djVw4EBKS0sTzZo1a5wu0lGrra1l6NChzJ0796Cv33vvvdx///3MnTuXlStX0q1bN84///zEbxx1FEdaT4ALL7yw2fZdvHjxcSzh0Xv77be55pprePfdd1myZAnRaJRx48ZRW1ubGKczbM+WrCd07O3Zo0cP7r77bt5//33ef/99xowZw8UXX5w4OHWG7QhHXk/o2NvxYFauXMmjjz7KkCFDmg1v121qvqLOOOMMM3PmzGbDTj75ZHPLLbc4VKL2d/vtt5uhQ4c6XYxjCjALFy5M9Nu2bbp162buvvvuxLCGhgaTnp5u/vCHPzhQwvax/3oaY8z06dPNxRdf7Eh5jpWysjIDmLffftsY03m35/7raUzn3J6ZmZnmj3/8Y6fdjk2a1tOYzrcdq6urTb9+/cySJUvMOeecY66//npjTPv/b34la0bC4TCrVq1i3LhxzYaPGzeOFStWOFSqY2PDhg3k5+fTu3dvLrvsMjZv3ux0kY6p4uJidu7c2Wzb+v1+zjnnnE63bQHeeustcnJyKCws5P/9v/9HWVmZ00U6KpWVlQBkZWUBnXd77r+eTTrL9ozFYjz77LPU1tYyatSoTrsd91/PJp1lOwJcc801fOMb3+DrX/96s+HtvU07xA/ltbfy8nJisRi5ubnNhufm5rJz506HStX+RowYwVNPPUVhYSG7du3irrvuYvTo0axdu5YuXbo4Xbxjomn7HWzbbt261YkiHTPjx4/nu9/9LgUFBRQXF/PLX/6SMWPGsGrVqg75BEhjDLNmzeJrX/sagwYNAjrn9jzYekLn2J5r1qxh1KhRNDQ0kJKSwsKFCxkwYEDi4NRZtuOh1hM6x3Zs8uyzz/LBBx+wcuXKA15r7//Nr2QYaWJZVrN+Y8wBwzqy8ePHJ7oHDx7MqFGjOPHEE/nTn/7ErFmzHCzZsdfZty3A5MmTE92DBg1i+PDhFBQUsGjRIiZNmuRgydrm2muv5cMPP+Sdd9454LXOtD0PtZ6dYXuedNJJFBUVsXfvXl544QWmT5/O22+/nXi9s2zHQ63ngAEDOsV2BNi2bRvXX389r732GoFA4JDjtdc2/UpepsnOzsbtdh9QC1JWVnZAyutMkpOTGTx4MBs2bHC6KMdM07eFvmrbFiAvL4+CgoIOuX1/8pOf8PLLL/Pmm2/So0ePxPDOtj0PtZ4H0xG3p8/no2/fvgwfPpw5c+YwdOhQHnzwwU63HQ+1ngfTEbcjwKpVqygrK2PYsGF4PB48Hg9vv/02Dz30EB6PJ7Hd2mubfiXDiM/nY9iwYSxZsqTZ8CVLljB69GiHSnXshUIh1q9fT15entNFOWZ69+5Nt27dmm3bcDjM22+/3am3LUBFRQXbtm3rUNvXGMO1117Liy++yBtvvEHv3r2bvd5ZtueR1vNgOuL23J8xhlAo1Gm246E0refBdNTtOHbsWNasWUNRUVGiGT58OFOnTqWoqIg+ffq07zY9qttsO7Bnn33WeL1e8/jjj5t169aZG264wSQnJ5stW7Y4XbR287Of/cy89dZbZvPmzebdd9813/zmN01qamqHX8fq6mqzevVqs3r1agOY+++/36xevdps3brVGGPM3XffbdLT082LL75o1qxZY773ve+ZvLw8U1VV5XDJW+dw61ldXW1+9rOfmRUrVpji4mLz5ptvmlGjRpnu3bt3qPX80Y9+ZNLT081bb71lSktLE01dXV1inM6wPY+0np1he86ePdssXbrUFBcXmw8//ND813/9l3G5XOa1114zxnSO7WjM4dezM2zHw9n32zTGtO82/cqGEWOM+Z//+R9TUFBgfD6fOe2005p9za4zmDx5ssnLyzNer9fk5+ebSZMmmbVr1zpdrKP25ptvGuCAZvr06caY+FfObr/9dtOtWzfj9/vN2WefbdasWeNsodvgcOtZV1dnxo0bZ7p27Wq8Xq/p1auXmT59uikpKXG62K1ysPUDzPz58xPjdIbteaT17Azb88orr0x8nnbt2tWMHTs2EUSM6Rzb0ZjDr2dn2I6Hs38Yac9tahljTBtqcERERETaxVfynhERERH58lAYEREREUcpjIiIiIijFEZERETEUQojIiIi4iiFEREREXGUwoiIiIg4SmFEREREHKUwIiIiIo5SGBERERFHKYyIiIiIoxRGRERExFH/HzDT/BUqPt1HAAAAAElFTkSuQmCC"},"metadata":{}}]},{"cell_type":"markdown","source":"It seems like the model performance has worsened. The default learning rate is good and giving better metrics so we will got with model 2","metadata":{}},{"cell_type":"code","source":"# let's make prediction\ny_probs = model_2.predict(test_data)\ny_probs[0]","metadata":{"execution":{"iopub.status.busy":"2023-07-24T11:22:41.783213Z","iopub.execute_input":"2023-07-24T11:22:41.783582Z","iopub.status.idle":"2023-07-24T11:22:42.321168Z","shell.execute_reply.started":"2023-07-24T11:22:41.783552Z","shell.execute_reply":"2023-07-24T11:22:42.320201Z"},"trusted":true},"execution_count":61,"outputs":[{"name":"stdout","text":"313/313 [==============================] - 0s 1ms/step\n","output_type":"stream"},{"execution_count":61,"output_type":"execute_result","data":{"text/plain":"array([0.00171238, 0.00134523, 0.00175137, 0.00129732, 0.00134885,\n       0.33605322, 0.00166967, 0.32184696, 0.1906197 , 0.14235528],\n      dtype=float32)"},"metadata":{}}]},{"cell_type":"code","source":"y_probs[0].argmax(), class_names[y_probs[0].argmax()]","metadata":{"execution":{"iopub.status.busy":"2023-07-24T11:24:05.602586Z","iopub.execute_input":"2023-07-24T11:24:05.602996Z","iopub.status.idle":"2023-07-24T11:24:05.610388Z","shell.execute_reply.started":"2023-07-24T11:24:05.602951Z","shell.execute_reply":"2023-07-24T11:24:05.609288Z"},"trusted":true},"execution_count":64,"outputs":[{"execution_count":64,"output_type":"execute_result","data":{"text/plain":"(5, 'Sandal')"},"metadata":{}}]},{"cell_type":"markdown","source":"We selected the max probability and the related class name and here is our ouput","metadata":{}},{"cell_type":"code","source":"y_preds = y_probs.argmax(axis = 1)   # converting to max probability for each class","metadata":{"execution":{"iopub.status.busy":"2023-07-24T11:25:10.847544Z","iopub.execute_input":"2023-07-24T11:25:10.847958Z","iopub.status.idle":"2023-07-24T11:25:10.853434Z","shell.execute_reply.started":"2023-07-24T11:25:10.847924Z","shell.execute_reply":"2023-07-24T11:25:10.852296Z"},"trusted":true},"execution_count":65,"outputs":[]},{"cell_type":"code","source":"y_preds[:10]","metadata":{"execution":{"iopub.status.busy":"2023-07-24T11:25:33.398021Z","iopub.execute_input":"2023-07-24T11:25:33.398395Z","iopub.status.idle":"2023-07-24T11:25:33.406198Z","shell.execute_reply.started":"2023-07-24T11:25:33.398358Z","shell.execute_reply":"2023-07-24T11:25:33.404841Z"},"trusted":true},"execution_count":67,"outputs":[{"execution_count":67,"output_type":"execute_result","data":{"text/plain":"array([5, 1, 1, 1, 1, 1, 1, 1, 5, 5])"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\ncm = confusion_matrix(y_true=test_labels, \n                 y_pred=y_preds)","metadata":{"execution":{"iopub.status.busy":"2023-07-24T11:27:47.942613Z","iopub.execute_input":"2023-07-24T11:27:47.943020Z","iopub.status.idle":"2023-07-24T11:27:47.951818Z","shell.execute_reply.started":"2023-07-24T11:27:47.942957Z","shell.execute_reply":"2023-07-24T11:27:47.950885Z"},"trusted":true},"execution_count":70,"outputs":[]},{"cell_type":"code","source":"cmd = ConfusionMatrixDisplay(cm)\ncmd.plot()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-07-24T11:28:08.241788Z","iopub.execute_input":"2023-07-24T11:28:08.242935Z","iopub.status.idle":"2023-07-24T11:28:08.695351Z","shell.execute_reply.started":"2023-07-24T11:28:08.242892Z","shell.execute_reply":"2023-07-24T11:28:08.694339Z"},"trusted":true},"execution_count":72,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 2 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"It seems like our model is confusing a lot and not performing well","metadata":{}},{"cell_type":"code","source":"tf.random.set_seed(42)\n\n# Create the model\nmodel_4 = tf.keras.Sequential([\n  tf.keras.layers.Flatten(input_shape=(28, 28)), \n  tf.keras.layers.Dense(4, activation=\"relu\"),\n  tf.keras.layers.Dense(4, activation=\"relu\"),\n  tf.keras.layers.Dense(10, activation=\"softmax\") ])\n\n# Compile the model\nmodel_4.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n                 optimizer=tf.keras.optimizers.Adam(lr=0.001), \n                 metrics=[\"accuracy\"])\n\n# Fit the model\nhistory = model_4.fit(train_data,\n                       train_labels,\n                       epochs=20,\n                       validation_data=(test_data, test_labels))","metadata":{"execution":{"iopub.status.busy":"2023-07-24T11:42:30.096863Z","iopub.execute_input":"2023-07-24T11:42:30.097956Z","iopub.status.idle":"2023-07-24T11:43:38.693441Z","shell.execute_reply.started":"2023-07-24T11:42:30.097908Z","shell.execute_reply":"2023-07-24T11:43:38.692354Z"},"trusted":true},"execution_count":84,"outputs":[{"name":"stdout","text":"Epoch 1/20\n1875/1875 [==============================] - 4s 2ms/step - loss: 1.2641 - accuracy: 0.5451 - val_loss: 0.8622 - val_accuracy: 0.7021\nEpoch 2/20\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.7694 - accuracy: 0.7283 - val_loss: 0.7266 - val_accuracy: 0.7385\nEpoch 3/20\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.6855 - accuracy: 0.7528 - val_loss: 0.6916 - val_accuracy: 0.7557\nEpoch 4/20\n1875/1875 [==============================] - 4s 2ms/step - loss: 0.6416 - accuracy: 0.7711 - val_loss: 0.6397 - val_accuracy: 0.7716\nEpoch 5/20\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.6179 - accuracy: 0.7824 - val_loss: 0.6440 - val_accuracy: 0.7710\nEpoch 6/20\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.6007 - accuracy: 0.7895 - val_loss: 0.6174 - val_accuracy: 0.7839\nEpoch 7/20\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.5899 - accuracy: 0.7933 - val_loss: 0.6142 - val_accuracy: 0.7812\nEpoch 8/20\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.5797 - accuracy: 0.7978 - val_loss: 0.5938 - val_accuracy: 0.7949\nEpoch 9/20\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.5708 - accuracy: 0.8002 - val_loss: 0.5896 - val_accuracy: 0.7949\nEpoch 10/20\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.5638 - accuracy: 0.8019 - val_loss: 0.5824 - val_accuracy: 0.7953\nEpoch 11/20\n1875/1875 [==============================] - 4s 2ms/step - loss: 0.5590 - accuracy: 0.8044 - val_loss: 0.5833 - val_accuracy: 0.7962\nEpoch 12/20\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.5519 - accuracy: 0.8073 - val_loss: 0.5760 - val_accuracy: 0.7981\nEpoch 13/20\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.5492 - accuracy: 0.8091 - val_loss: 0.5775 - val_accuracy: 0.8043\nEpoch 14/20\n1875/1875 [==============================] - 4s 2ms/step - loss: 0.5445 - accuracy: 0.8115 - val_loss: 0.5714 - val_accuracy: 0.8046\nEpoch 15/20\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.5411 - accuracy: 0.8126 - val_loss: 0.5764 - val_accuracy: 0.8016\nEpoch 16/20\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.5379 - accuracy: 0.8151 - val_loss: 0.5765 - val_accuracy: 0.8018\nEpoch 17/20\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.5346 - accuracy: 0.8158 - val_loss: 0.5776 - val_accuracy: 0.8017\nEpoch 18/20\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.5332 - accuracy: 0.8166 - val_loss: 0.5679 - val_accuracy: 0.8044\nEpoch 19/20\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.5310 - accuracy: 0.8169 - val_loss: 0.5800 - val_accuracy: 0.7977\nEpoch 20/20\n1875/1875 [==============================] - 3s 2ms/step - loss: 0.5281 - accuracy: 0.8185 - val_loss: 0.5912 - val_accuracy: 0.7982\n","output_type":"stream"}]},{"cell_type":"code","source":"pd.DataFrame(history.history).plot()","metadata":{"execution":{"iopub.status.busy":"2023-07-24T11:43:49.522794Z","iopub.execute_input":"2023-07-24T11:43:49.523536Z","iopub.status.idle":"2023-07-24T11:43:49.854098Z","shell.execute_reply.started":"2023-07-24T11:43:49.523498Z","shell.execute_reply":"2023-07-24T11:43:49.853026Z"},"trusted":true},"execution_count":86,"outputs":[{"execution_count":86,"output_type":"execute_result","data":{"text/plain":"<Axes: >"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}]},{"cell_type":"code","source":"y_probs = model_4.predict(test_data)\ny_preds = y_probs.argmax(axis = 1)\ncm = confusion_matrix(y_true=test_labels, \n                 y_pred=y_preds)","metadata":{"execution":{"iopub.status.busy":"2023-07-24T11:44:33.981116Z","iopub.execute_input":"2023-07-24T11:44:33.981524Z","iopub.status.idle":"2023-07-24T11:44:34.729682Z","shell.execute_reply.started":"2023-07-24T11:44:33.981466Z","shell.execute_reply":"2023-07-24T11:44:34.728515Z"},"trusted":true},"execution_count":89,"outputs":[{"name":"stdout","text":"313/313 [==============================] - 0s 1ms/step\n","output_type":"stream"}]},{"cell_type":"code","source":"cmd = ConfusionMatrixDisplay(cm, display_labels = class_names)\ncmd.plot(xticks_rotation = \"vertical\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-07-24T11:56:13.307756Z","iopub.execute_input":"2023-07-24T11:56:13.308161Z","iopub.status.idle":"2023-07-24T11:56:13.787357Z","shell.execute_reply.started":"2023-07-24T11:56:13.308126Z","shell.execute_reply":"2023-07-24T11:56:13.786203Z"},"trusted":true},"execution_count":115,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 2 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"Well, our model has improved after adding lr in Adam optimizer but still it is confusing with some classes. Maybe we can add more data and train or add layers and train. ","metadata":{}},{"cell_type":"code","source":"model_4.summary()","metadata":{"execution":{"iopub.status.busy":"2023-07-24T11:57:38.231358Z","iopub.execute_input":"2023-07-24T11:57:38.231769Z","iopub.status.idle":"2023-07-24T11:57:38.253461Z","shell.execute_reply.started":"2023-07-24T11:57:38.231737Z","shell.execute_reply":"2023-07-24T11:57:38.252363Z"},"trusted":true},"execution_count":117,"outputs":[{"name":"stdout","text":"Model: \"sequential_13\"\n_________________________________________________________________\n Layer (type)                Output Shape              Param #   \n=================================================================\n flatten_13 (Flatten)        (None, 784)               0         \n                                                                 \n dense_38 (Dense)            (None, 4)                 3140      \n                                                                 \n dense_39 (Dense)            (None, 4)                 20        \n                                                                 \n dense_40 (Dense)            (None, 10)                50        \n                                                                 \n=================================================================\nTotal params: 3,210\nTrainable params: 3,210\nNon-trainable params: 0\n_________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"code","source":"model_4.layers","metadata":{"execution":{"iopub.status.busy":"2023-07-24T11:58:05.606704Z","iopub.execute_input":"2023-07-24T11:58:05.607091Z","iopub.status.idle":"2023-07-24T11:58:05.614691Z","shell.execute_reply.started":"2023-07-24T11:58:05.607058Z","shell.execute_reply":"2023-07-24T11:58:05.613460Z"},"trusted":true},"execution_count":121,"outputs":[{"execution_count":121,"output_type":"execute_result","data":{"text/plain":"[<keras.layers.reshaping.flatten.Flatten at 0x7bcf52e143d0>,\n <keras.layers.core.dense.Dense at 0x7bcf40d57580>,\n <keras.layers.core.dense.Dense at 0x7bcf5423b430>,\n <keras.layers.core.dense.Dense at 0x7bcf5423ae00>]"},"metadata":{}}]},{"cell_type":"code","source":"weights, biases = model_4.layers[1].get_weights()","metadata":{"execution":{"iopub.status.busy":"2023-07-24T11:59:29.276086Z","iopub.execute_input":"2023-07-24T11:59:29.276458Z","iopub.status.idle":"2023-07-24T11:59:29.282294Z","shell.execute_reply.started":"2023-07-24T11:59:29.276430Z","shell.execute_reply":"2023-07-24T11:59:29.281209Z"},"trusted":true},"execution_count":127,"outputs":[]},{"cell_type":"code","source":"weights","metadata":{"execution":{"iopub.status.busy":"2023-07-24T11:59:33.411726Z","iopub.execute_input":"2023-07-24T11:59:33.412114Z","iopub.status.idle":"2023-07-24T11:59:33.419954Z","shell.execute_reply.started":"2023-07-24T11:59:33.412084Z","shell.execute_reply":"2023-07-24T11:59:33.418849Z"},"trusted":true},"execution_count":128,"outputs":[{"execution_count":128,"output_type":"execute_result","data":{"text/plain":"array([[-0.02335919,  0.11687471, -0.0063143 ,  0.7371094 ],\n       [ 0.662387  , -0.29908442, -0.1483147 ,  0.90486205],\n       [ 0.19810879, -0.17045577,  0.5598243 ,  1.0724595 ],\n       ...,\n       [ 0.37192702,  0.14758348,  0.45291683, -0.5017668 ],\n       [ 0.40469497,  0.8176526 ,  0.3886368 , -0.49738556],\n       [-0.2500754 , -0.0461604 ,  1.0552307 ,  0.29275826]],\n      dtype=float32)"},"metadata":{}}]},{"cell_type":"code","source":"biases","metadata":{"execution":{"iopub.status.busy":"2023-07-24T11:59:37.031140Z","iopub.execute_input":"2023-07-24T11:59:37.031518Z","iopub.status.idle":"2023-07-24T11:59:37.038875Z","shell.execute_reply.started":"2023-07-24T11:59:37.031488Z","shell.execute_reply":"2023-07-24T11:59:37.037651Z"},"trusted":true},"execution_count":129,"outputs":[{"execution_count":129,"output_type":"execute_result","data":{"text/plain":"array([3.0391958 , 1.586256  , 0.84263223, 1.5568676 ], dtype=float32)"},"metadata":{}}]},{"cell_type":"code","source":"keras.utils.plot_model(model_4, show_shapes = True)","metadata":{"execution":{"iopub.status.busy":"2023-07-24T12:00:15.576616Z","iopub.execute_input":"2023-07-24T12:00:15.577013Z","iopub.status.idle":"2023-07-24T12:00:15.645632Z","shell.execute_reply.started":"2023-07-24T12:00:15.576967Z","shell.execute_reply":"2023-07-24T12:00:15.644499Z"},"trusted":true},"execution_count":131,"outputs":[{"execution_count":131,"output_type":"execute_result","data":{"image/png":"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","text/plain":"<IPython.core.display.Image 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