{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","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":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-04-12T02:16:50.780764Z","iopub.execute_input":"2023-04-12T02:16:50.781446Z","iopub.status.idle":"2023-04-12T02:16:57.729985Z","shell.execute_reply.started":"2023-04-12T02:16:50.781412Z","shell.execute_reply":"2023-04-12T02:16:57.728919Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"/kaggle/input/rfcx-species-audio-detection/sample_submission.csv\n/kaggle/input/rfcx-species-audio-detection/train_fp.csv\n/kaggle/input/rfcx-species-audio-detection/train_tp.csv\n/kaggle/input/rfcx-species-audio-detection/tfrecords/test/18-63.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/test/08-63.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/test/13-63.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/test/11-63.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/test/02-63.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/test/07-63.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/test/27-63.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/test/23-63.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/test/04-63.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/test/30-63.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/test/01-63.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/test/00-63.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/test/29-63.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/test/03-63.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/test/15-63.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/test/12-63.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/test/31-39.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/test/17-63.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/test/28-63.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/test/05-63.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/test/21-63.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/test/26-63.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/test/16-63.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/test/10-63.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/test/25-63.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/test/24-63.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/test/22-63.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/test/19-63.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/test/20-63.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/test/09-63.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/test/14-63.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/test/06-63.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/train/09-148.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/train/02-148.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/train/18-148.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/train/12-148.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/train/11-148.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/train/07-148.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/train/15-148.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/train/31-139.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/train/05-148.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/train/19-148.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/train/16-148.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/train/22-148.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/train/00-148.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/train/29-148.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/train/10-148.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/train/04-148.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/train/13-148.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/train/25-148.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/train/08-148.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/train/24-148.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/train/27-148.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/train/26-148.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/train/03-148.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/train/17-148.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/train/23-148.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/train/28-148.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/train/21-148.tfrec\n/kaggle/input/rfcx-species-audio-detection/tfrecords/train/20-148.tfrec\n/kaggle/input/rfcx-species-aud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lac\n","output_type":"stream"}]},{"cell_type":"code","source":"import pandas as pd\nimport os\nimport librosa\nimport librosa.display\nimport matplotlib.pyplot as plt\nfrom sklearn.preprocessing import normalize\nimport warnings\nwarnings.filterwarnings('ignore')\nimport numpy as np\nimport pickle\nimport joblib\nfrom sklearn.model_selection import train_test_split\nfrom tensorflow.keras import models, layers\nimport tensorflow as tf","metadata":{"execution":{"iopub.status.busy":"2023-04-12T08:45:09.009712Z","iopub.execute_input":"2023-04-12T08:45:09.010325Z","iopub.status.idle":"2023-04-12T08:45:17.655195Z","shell.execute_reply.started":"2023-04-12T08:45:09.010286Z","shell.execute_reply":"2023-04-12T08:45:17.654026Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"os.chdir('/kaggle/input/rfcx-species-audio-detection')\ndf = pd.read_csv('train_tp.csv')","metadata":{"execution":{"iopub.status.busy":"2023-04-12T08:45:21.288699Z","iopub.execute_input":"2023-04-12T08:45:21.289763Z","iopub.status.idle":"2023-04-12T08:45:21.311513Z","shell.execute_reply.started":"2023-04-12T08:45:21.289721Z","shell.execute_reply":"2023-04-12T08:45:21.31055Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"df.head()","metadata":{"execution":{"iopub.status.busy":"2023-04-12T08:45:23.597621Z","iopub.execute_input":"2023-04-12T08:45:23.598021Z","iopub.status.idle":"2023-04-12T08:45:23.622212Z","shell.execute_reply.started":"2023-04-12T08:45:23.597982Z","shell.execute_reply":"2023-04-12T08:45:23.621042Z"},"trusted":true},"execution_count":3,"outputs":[{"execution_count":3,"output_type":"execute_result","data":{"text/plain":"  recording_id  species_id  songtype_id    t_min     f_min    t_max     f_max\n0    003bec244          14            1  44.5440  2531.250  45.1307   5531.25\n1    006ab765f          23            1  39.9615  7235.160  46.0452  11283.40\n2    007f87ba2          12            1  39.1360   562.500  42.2720   3281.25\n3    0099c367b          17            4  51.4206  1464.260  55.1996   4565.04\n4    009b760e6          10            1  50.0854   947.461  52.5293  10852.70","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>recording_id</th>\n      <th>species_id</th>\n      <th>songtype_id</th>\n      <th>t_min</th>\n      <th>f_min</th>\n      <th>t_max</th>\n      <th>f_max</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>003bec244</td>\n      <td>14</td>\n      <td>1</td>\n      <td>44.5440</td>\n      <td>2531.250</td>\n      <td>45.1307</td>\n      <td>5531.25</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>006ab765f</td>\n      <td>23</td>\n      <td>1</td>\n      <td>39.9615</td>\n      <td>7235.160</td>\n      <td>46.0452</td>\n      <td>11283.40</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>007f87ba2</td>\n      <td>12</td>\n      <td>1</td>\n      <td>39.1360</td>\n      <td>562.500</td>\n      <td>42.2720</td>\n      <td>3281.25</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0099c367b</td>\n      <td>17</td>\n      <td>4</td>\n      <td>51.4206</td>\n      <td>1464.260</td>\n      <td>55.1996</td>\n      <td>4565.04</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>009b760e6</td>\n      <td>10</td>\n      <td>1</td>\n      <td>50.0854</td>\n      <td>947.461</td>\n      <td>52.5293</td>\n      <td>10852.70</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"sample_num=3 #pick a file to display\n#get the filename \nfilename=df.recording_id[sample_num]+str('.flac')\n#define the beginning time of the signal\ntstart = df.t_min[sample_num] \ntend = df.t_max[sample_num] #define the end time of the signal\ny,sr=librosa.load('train/'+str(filename)) #load the file\nlibrosa.display.waveshow(y,sr=sr, x_axis='time', color='cyan')","metadata":{"execution":{"iopub.status.busy":"2023-04-12T08:45:25.49755Z","iopub.execute_input":"2023-04-12T08:45:25.49791Z","iopub.status.idle":"2023-04-12T08:45:36.470876Z","shell.execute_reply.started":"2023-04-12T08:45:25.497876Z","shell.execute_reply":"2023-04-12T08:45:36.469768Z"},"trusted":true},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"<librosa.display.AdaptiveWaveplot at 0x7d478f8df390>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":"def padding(array, xx, yy):\n    \"\"\"\n    :param array: numpy array\n    :param xx: desired height\n    :param yy: desirex width\n    :return: padded array\n    \"\"\"\n\n    h = array.shape[0]\n    w = array.shape[1]\n\n    a = (xx - h) // 2\n    aa = xx - a - h\n\n    b = (yy - w) // 2\n    bb = yy - b - w\n\n    return np.pad(array, pad_width=((a, aa), (b, bb)), mode='constant')","metadata":{"execution":{"iopub.status.busy":"2023-04-12T08:45:36.489558Z","iopub.execute_input":"2023-04-12T08:45:36.490274Z","iopub.status.idle":"2023-04-12T08:45:36.499597Z","shell.execute_reply.started":"2023-04-12T08:45:36.490231Z","shell.execute_reply":"2023-04-12T08:45:36.498589Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"def generate_features(y_cut):\n    max_size=1000 #my max audio file feature width\n    stft = padding(np.abs(librosa.stft(y_cut, n_fft=255, hop_length        = 512)), 128, max_size)\n    MFCCs = padding(librosa.feature.mfcc(y=y_cut,n_mfcc=128,n_fft=255, hop_length=512),128,max_size)\n    spec_centroid = librosa.feature.spectral_centroid(y=y_cut, sr=sr)\n    chroma_stft = librosa.feature.chroma_stft(y=y_cut, sr=sr)\n    spec_bw = librosa.feature.spectral_bandwidth(y=y_cut, sr=sr)\n    #Now the padding part\n    image = np.array([padding(normalize(spec_bw),1, max_size)]).reshape(1,max_size)\n    image = np.append(image,padding(normalize(spec_centroid),1, max_size), axis=0) \n#repeat the padded spec_bw,spec_centroid and chroma stft until they are stft and MFCC-sized\n    for i in range(0,9):\n        image = np.append(image,padding(normalize(spec_bw),1, max_size), axis=0)\n        image = np.append(image, padding(normalize(spec_centroid),1, max_size), axis=0)\n        image = np.append(image, padding(normalize(chroma_stft),12, max_size), axis=0)\n    image=np.dstack((image,np.abs(stft)))\n    image=np.dstack((image,MFCCs))\n    return image","metadata":{"execution":{"iopub.status.busy":"2023-04-12T08:45:36.502061Z","iopub.execute_input":"2023-04-12T08:45:36.503058Z","iopub.status.idle":"2023-04-12T08:45:36.518292Z","shell.execute_reply.started":"2023-04-12T08:45:36.503019Z","shell.execute_reply":"2023-04-12T08:45:36.516515Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"X=df.drop('species_id',axis=1)\ny=df.species_id","metadata":{"execution":{"iopub.status.busy":"2023-04-12T07:39:17.364068Z","iopub.execute_input":"2023-04-12T07:39:17.365185Z","iopub.status.idle":"2023-04-12T07:39:17.377494Z","shell.execute_reply.started":"2023-04-12T07:39:17.365134Z","shell.execute_reply":"2023-04-12T07:39:17.376398Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"#Split once to get the test and training set\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=123, stratify=y)\nprint(X_train.shape,X_test.shape)","metadata":{"execution":{"iopub.status.busy":"2023-04-12T07:39:20.002864Z","iopub.execute_input":"2023-04-12T07:39:20.003926Z","iopub.status.idle":"2023-04-12T07:39:20.020172Z","shell.execute_reply.started":"2023-04-12T07:39:20.003852Z","shell.execute_reply":"2023-04-12T07:39:20.018767Z"},"trusted":true},"execution_count":10,"outputs":[{"name":"stdout","text":"(912, 6) (304, 6)\n","output_type":"stream"}]},{"cell_type":"code","source":"#Split twice to get the validation set\nX_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.25, random_state=123)\n","metadata":{"execution":{"iopub.status.busy":"2023-04-12T07:39:29.18821Z","iopub.execute_input":"2023-04-12T07:39:29.188803Z","iopub.status.idle":"2023-04-12T07:39:29.197164Z","shell.execute_reply.started":"2023-04-12T07:39:29.188763Z","shell.execute_reply":"2023-04-12T07:39:29.195552Z"},"trusted":true},"execution_count":11,"outputs":[]},{"cell_type":"code","source":"print(X_train.shape, X_test.shape, X_val.shape, len(y_train), len(y_test), len(y_val))","metadata":{"execution":{"iopub.status.busy":"2023-04-12T07:39:31.213321Z","iopub.execute_input":"2023-04-12T07:39:31.214128Z","iopub.status.idle":"2023-04-12T07:39:31.220805Z","shell.execute_reply.started":"2023-04-12T07:39:31.214087Z","shell.execute_reply":"2023-04-12T07:39:31.219619Z"},"trusted":true},"execution_count":12,"outputs":[{"name":"stdout","text":"(684, 6) (304, 6) (228, 6) 684 304 228\n","output_type":"stream"}]},{"cell_type":"code","source":"def get_features(df_in):   \n    features=[]     \n    labels = [] #empty array to store labels     \n    #For each species, determine how many augmentations are needed\n    df_in=df_in.reset_index() \n    filelist=[]\n    for i in df_in.species_id.unique():  \n    #all the file indices with the same species_id     \n        \n        filelist.append(df_in.loc[df_in.species_id == i].index)\n    for j in range(0,len(filelist)):\n        for k in range(0,len(filelist[j])):\n            filename = df_in.iloc[filelist[j][k]].recording_id+str('.flac') #get the filename   \n                #define the beginning time of the signal          \n            tstart = df_in.iloc[filelist[j][k]].t_min             \n            tend = df_in.iloc[filelist[j][k]].t_max #end of signal\n            recording_id = df_in.iloc[filelist[j][k]].recording_id\n            species_id = df_in.iloc[filelist[j][k]].species_id\n            songtype_id = df_in.iloc[filelist[j][k]].songtype_id   \n                #Load the file\n            y, sr = librosa.load(f'/kaggle/input/rfcx-species-audio-detection/train/{filename}',sr=28000)  \n                #cut the file to signal start and end  \n            y_cut=y[int(round(tstart*sr)):int(round(tend*sr))]  \n                #generate features & output numpy array          \n            data = generate_features(y_cut) \n            features.append(data[np.newaxis,...])    \n            labels.append(species_id)     \n    output=np.concatenate(features,axis=0)  \n    \n    return(np.array(output), labels)\n#use get_features to calculate and store the features\ntest_features, test_labels = get_features(pd.concat([X_test,y_test],axis=1))\n","metadata":{"execution":{"iopub.status.busy":"2023-04-12T07:39:39.979059Z","iopub.execute_input":"2023-04-12T07:39:39.980094Z","iopub.status.idle":"2023-04-12T07:40:51.778602Z","shell.execute_reply.started":"2023-04-12T07:39:39.980055Z","shell.execute_reply":"2023-04-12T07:40:51.777353Z"},"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"code","source":"train_features, train_labels = get_features(pd.concat([X_train,y_train],axis=1))\nval_features, val_labels = get_features(pd.concat([X_val,y_val],axis=1))","metadata":{"execution":{"iopub.status.busy":"2023-04-12T07:40:51.780829Z","iopub.execute_input":"2023-04-12T07:40:51.781556Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(X_train.shape, X_test.shape, X_val.shape, len(y_train), len(y_test), len(y_val))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_features.shape","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_train = np.array((train_features-np.min(train_features))/(np.max(train_features)-np.min(train_features)))\nX_test = np.array((test_features-np.min(test_features))/(np.max(test_features)-np.min(test_features)))\nX_val = np.array((val_features-np.min(val_features))/(np.max(val_features)-np.min(val_features)))\nX_train = X_train/np.std(X_train)\nX_test = X_test/np.std(X_test)\nX_val = X_val/np.std(X_val)  \ny_train = np.array(train_labels)\ny_test = np.array(test_labels)\ny_val = np.array(val_labels)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(X_train.shape, X_test.shape, X_val.shape, len(y_train), len(y_test), len(y_val))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"input_shape=(128,1000,3)\nCNNmodel = models.Sequential()\nCNNmodel.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=input_shape))\nCNNmodel.add(layers.MaxPooling2D((2, 2)))\nCNNmodel.add(layers.Flatten())\nCNNmodel.add(layers.Dense(32, activation='relu'))\nCNNmodel.add(layers.Dense(24, activation='softmax'))\nCNNmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2023-04-12T02:23:22.788023Z","iopub.execute_input":"2023-04-12T02:23:22.789007Z","iopub.status.idle":"2023-04-12T02:23:26.628306Z","shell.execute_reply.started":"2023-04-12T02:23:22.788962Z","shell.execute_reply":"2023-04-12T02:23:26.627524Z"},"trusted":true},"execution_count":19,"outputs":[{"name":"stdout","text":"Model: \"sequential\"\n_________________________________________________________________\n Layer (type)                Output Shape              Param #   \n=================================================================\n conv2d (Conv2D)             (None, 126, 998, 32)      896       \n                                                                 \n max_pooling2d (MaxPooling2D  (None, 63, 499, 32)      0         \n )                                                               \n                                                                 \n flatten (Flatten)           (None, 1005984)           0         \n                                                                 \n dense (Dense)               (None, 32)                32191520  \n                                                                 \n dense_1 (Dense)             (None, 24)                792       \n                                                                 \n=================================================================\nTotal params: 32,193,208\nTrainable params: 32,193,208\nNon-trainable params: 0\n_________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"code","source":"input_shape=(128,1000,3)\nCNNmodel = models.Sequential()\nCNNmodel.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=input_shape, padding='same'))\nCNNmodel.add(layers.MaxPooling2D((2, 2)))\nCNNmodel.add(layers.Dropout(0.2))\nCNNmodel.add(layers.Conv2D(64, (3, 3), activation='relu', padding='same'))\nCNNmodel.add(layers.MaxPooling2D((2, 2)))\nCNNmodel.add(layers.Dropout(0.2))\nCNNmodel.add(layers.Conv2D(128, (3, 3), activation='relu', padding='same'))\nCNNmodel.add(layers.MaxPooling2D((2, 2)))\nCNNmodel.add(layers.Dropout(0.2))\nCNNmodel.add(layers.Conv2D(256, (3, 3), activation='relu', padding='same'))\nCNNmodel.add(layers.MaxPooling2D((2, 2)))\nCNNmodel.add(layers.Dropout(0.2))\nCNNmodel.add(layers.Conv2D(512, (3, 3), activation='relu', padding='same'))\nCNNmodel.add(layers.MaxPooling2D((2, 2)))\nCNNmodel.add(layers.Dropout(0.2))\nCNNmodel.add(layers.Conv2D(1024, (3, 3), activation='relu', padding='same'))\nCNNmodel.add(layers.MaxPooling2D((2, 2)))\nCNNmodel.add(layers.Dropout(0.2))\nCNNmodel.add(layers.Flatten())\nCNNmodel.add(layers.Dense(1024, activation='relu'))\nCNNmodel.add(layers.Dropout(0.2))\nCNNmodel.add(layers.Dense(512, activation='relu'))\nCNNmodel.add(layers.Dense(24, activation='softmax'))\n","metadata":{"execution":{"iopub.status.busy":"2023-04-12T02:23:26.629894Z","iopub.execute_input":"2023-04-12T02:23:26.630264Z","iopub.status.idle":"2023-04-12T02:23:26.809627Z","shell.execute_reply.started":"2023-04-12T02:23:26.630227Z","shell.execute_reply":"2023-04-12T02:23:26.808522Z"},"trusted":true},"execution_count":20,"outputs":[]},{"cell_type":"code","source":"CNNmodel.compile(optimizer='adam',loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),metrics=['accuracy'])","metadata":{"execution":{"iopub.status.busy":"2023-04-12T02:24:02.692183Z","iopub.execute_input":"2023-04-12T02:24:02.692571Z","iopub.status.idle":"2023-04-12T02:24:02.70761Z","shell.execute_reply.started":"2023-04-12T02:24:02.69254Z","shell.execute_reply":"2023-04-12T02:24:02.706332Z"},"trusted":true},"execution_count":22,"outputs":[]},{"cell_type":"code","source":"history = CNNmodel.fit(X_train, y_train, epochs=50, validation_data= (X_val, y_val))","metadata":{"execution":{"iopub.status.busy":"2023-04-12T02:25:20.185861Z","iopub.execute_input":"2023-04-12T02:25:20.186997Z"},"trusted":true},"execution_count":null,"outputs":[{"name":"stdout","text":"Epoch 1/50\n","output_type":"stream"},{"name":"stderr","text":"2023-04-12 02:25:24.868350: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:954] layout failed: INVALID_ARGUMENT: Size of values 0 does not match size of permutation 4 @ fanin shape insequential_1/dropout/dropout/SelectV2-2-TransposeNHWCToNCHW-LayoutOptimizer\n","output_type":"stream"},{"name":"stdout","text":"22/22 [==============================] - 27s 499ms/step - loss: 30.4619 - accuracy: 0.0629 - val_loss: 3.1763 - val_accuracy: 0.1053\nEpoch 2/50\n22/22 [==============================] - 6s 280ms/step - loss: 3.1678 - accuracy: 0.0746 - val_loss: 3.1757 - val_accuracy: 0.1053\nEpoch 3/50\n22/22 [==============================] - 6s 288ms/step - loss: 3.1658 - accuracy: 0.0746 - val_loss: 3.1736 - val_accuracy: 0.1053\nEpoch 4/50\n22/22 [==============================] - 6s 288ms/step - loss: 3.1611 - accuracy: 0.0746 - val_loss: 3.1761 - val_accuracy: 0.1053\nEpoch 5/50\n22/22 [==============================] - 6s 285ms/step - loss: 3.1622 - accuracy: 0.0746 - val_loss: 3.1765 - val_accuracy: 0.1053\nEpoch 6/50\n22/22 [==============================] - 6s 291ms/step - loss: 3.1619 - accuracy: 0.0746 - val_loss: 3.1750 - val_accuracy: 0.1053\nEpoch 7/50\n22/22 [==============================] - 6s 287ms/step - loss: 3.1599 - accuracy: 0.0746 - val_loss: 3.1757 - val_accuracy: 0.1053\nEpoch 8/50\n22/22 [==============================] - 6s 288ms/step - loss: 3.1574 - accuracy: 0.0746 - val_loss: 3.1768 - val_accuracy: 0.1053\nEpoch 9/50\n22/22 [==============================] - 6s 288ms/step - loss: 3.1565 - accuracy: 0.0746 - val_loss: 3.1765 - val_accuracy: 0.1053\nEpoch 10/50\n22/22 [==============================] - 6s 288ms/step - loss: 3.1578 - accuracy: 0.0746 - val_loss: 3.1760 - val_accuracy: 0.1053\nEpoch 11/50\n22/22 [==============================] - 6s 283ms/step - loss: 3.1614 - accuracy: 0.0746 - val_loss: 3.1764 - val_accuracy: 0.1053\nEpoch 12/50\n22/22 [==============================] - 6s 280ms/step - loss: 3.1570 - accuracy: 0.0746 - val_loss: 3.1767 - val_accuracy: 0.1053\nEpoch 13/50\n22/22 [==============================] - 6s 282ms/step - loss: 3.1589 - accuracy: 0.0760 - val_loss: 3.1775 - val_accuracy: 0.1053\nEpoch 14/50\n22/22 [==============================] - 6s 281ms/step - loss: 3.1593 - accuracy: 0.0746 - val_loss: 3.1787 - val_accuracy: 0.1053\nEpoch 15/50\n22/22 [==============================] - 6s 285ms/step - loss: 3.1573 - accuracy: 0.0731 - val_loss: 3.1780 - val_accuracy: 0.1053\nEpoch 16/50\n22/22 [==============================] - 6s 283ms/step - loss: 3.1573 - accuracy: 0.0746 - val_loss: 3.1779 - val_accuracy: 0.1053\nEpoch 17/50\n22/22 [==============================] - 6s 290ms/step - loss: 3.1567 - accuracy: 0.0746 - val_loss: 3.1746 - val_accuracy: 0.1053\nEpoch 18/50\n22/22 [==============================] - 6s 290ms/step - loss: 3.1545 - accuracy: 0.0746 - val_loss: 3.1774 - val_accuracy: 0.1053\nEpoch 19/50\n22/22 [==============================] - 6s 282ms/step - loss: 3.1551 - accuracy: 0.0746 - val_loss: 3.1775 - val_accuracy: 0.1053\nEpoch 20/50\n22/22 [==============================] - 6s 291ms/step - loss: 3.1572 - accuracy: 0.0746 - val_loss: 3.1774 - val_accuracy: 0.1053\nEpoch 21/50\n22/22 [==============================] - 6s 284ms/step - loss: 3.1596 - accuracy: 0.0746 - val_loss: 3.1792 - val_accuracy: 0.1053\nEpoch 22/50\n22/22 [==============================] - 6s 283ms/step - loss: 3.1547 - accuracy: 0.0746 - val_loss: 3.1824 - val_accuracy: 0.1053\nEpoch 23/50\n22/22 [==============================] - 6s 283ms/step - loss: 3.1545 - accuracy: 0.0746 - val_loss: 3.1831 - val_accuracy: 0.1053\nEpoch 24/50\n22/22 [==============================] - 6s 282ms/step - loss: 3.1551 - accuracy: 0.0746 - val_loss: 3.1824 - val_accuracy: 0.1053\nEpoch 25/50\n22/22 [==============================] - 6s 285ms/step - loss: 3.1540 - accuracy: 0.0746 - val_loss: 3.1833 - val_accuracy: 0.1053\nEpoch 26/50\n22/22 [==============================] - 6s 285ms/step - loss: 3.1536 - accuracy: 0.0746 - val_loss: 3.1816 - val_accuracy: 0.1053\nEpoch 27/50\n22/22 [==============================] - 6s 291ms/step - loss: 3.1532 - accuracy: 0.0746 - val_loss: 3.1835 - val_accuracy: 0.1053\nEpoch 28/50\n22/22 [==============================] - 6s 284ms/step - loss: 3.1568 - accuracy: 0.0746 - val_loss: 3.1821 - val_accuracy: 0.1053\nEpoch 29/50\n22/22 [==============================] - 6s 283ms/step - loss: 3.1548 - accuracy: 0.0746 - val_loss: 3.1845 - val_accuracy: 0.1053\nEpoch 30/50\n22/22 [==============================] - 6s 284ms/step - loss: 3.1531 - accuracy: 0.0746 - val_loss: 3.1830 - val_accuracy: 0.1053\nEpoch 31/50\n22/22 [==============================] - 6s 285ms/step - loss: 3.1542 - accuracy: 0.0746 - val_loss: 3.1839 - val_accuracy: 0.1053\nEpoch 32/50\n22/22 [==============================] - 6s 285ms/step - loss: 3.1543 - accuracy: 0.0746 - val_loss: 3.1846 - val_accuracy: 0.1053\nEpoch 33/50\n22/22 [==============================] - 6s 284ms/step - loss: 3.1533 - accuracy: 0.0746 - val_loss: 3.1837 - val_accuracy: 0.1053\nEpoch 34/50\n22/22 [==============================] - 6s 283ms/step - loss: 3.1531 - accuracy: 0.0746 - val_loss: 3.1850 - val_accuracy: 0.1053\nEpoch 35/50\n22/22 [==============================] - 6s 285ms/step - loss: 3.1531 - accuracy: 0.0746 - val_loss: 3.1853 - val_accuracy: 0.1053\nEpoch 36/50\n22/22 [==============================] - 6s 295ms/step - loss: 3.1541 - accuracy: 0.0746 - val_loss: 3.1848 - val_accuracy: 0.1053\nEpoch 37/50\n22/22 [==============================] - 6s 292ms/step - loss: 3.1532 - accuracy: 0.0746 - val_loss: 3.1850 - val_accuracy: 0.1053\nEpoch 38/50\n22/22 [==============================] - 6s 283ms/step - loss: 3.1533 - accuracy: 0.0746 - val_loss: 3.1862 - val_accuracy: 0.1053\nEpoch 39/50\n22/22 [==============================] - 6s 284ms/step - loss: 3.1537 - accuracy: 0.0746 - val_loss: 3.1827 - val_accuracy: 0.1053\nEpoch 40/50\n22/22 [==============================] - 6s 288ms/step - loss: 3.1538 - accuracy: 0.0746 - val_loss: 3.1844 - val_accuracy: 0.1053\nEpoch 41/50\n22/22 [==============================] - 6s 286ms/step - loss: 3.1533 - accuracy: 0.0746 - val_loss: 3.1831 - val_accuracy: 0.1053\nEpoch 42/50\n22/22 [==============================] - 6s 294ms/step - loss: 3.1530 - accuracy: 0.0746 - val_loss: 3.1858 - val_accuracy: 0.1053\nEpoch 43/50\n22/22 [==============================] - 6s 294ms/step - loss: 3.1524 - accuracy: 0.0746 - val_loss: 3.1848 - val_accuracy: 0.1053\nEpoch 44/50\n22/22 [==============================] - 6s 293ms/step - loss: 3.1533 - accuracy: 0.0746 - val_loss: 3.1869 - val_accuracy: 0.1053\nEpoch 45/50\n22/22 [==============================] - 6s 296ms/step - loss: 3.1542 - accuracy: 0.0746 - val_loss: 3.1875 - val_accuracy: 0.1053\nEpoch 46/50\n22/22 [==============================] - 6s 285ms/step - loss: 3.1525 - accuracy: 0.0746 - val_loss: 3.1849 - val_accuracy: 0.1053\nEpoch 47/50\n22/22 [==============================] - 6s 285ms/step - loss: 3.1539 - accuracy: 0.0746 - val_loss: 3.1855 - val_accuracy: 0.1053\nEpoch 48/50\n22/22 [==============================] - 6s 293ms/step - loss: 3.1525 - accuracy: 0.0746 - val_loss: 3.1840 - val_accuracy: 0.1053\nEpoch 49/50\n22/22 [==============================] - 6s 294ms/step - loss: 3.1540 - accuracy: 0.0746 - val_loss: 3.1856 - val_accuracy: 0.1053\nEpoch 50/50\n22/22 [==============================] - 6s 294ms/step - loss: 3.1537 - accuracy: 0.0746 - val_loss: 3.1877 - val_accuracy: 0.1053\n","output_type":"stream"}]},{"cell_type":"code","source":"#Adapted from Deep Learning with Python by Francois Chollet, 2018\nhistory_dict=history.history\nloss_values=history_dict['loss']\nacc_values=history_dict['accuracy']\nval_loss_values = history_dict['val_loss']\nval_acc_values=history_dict['val_accuracy']\nepochs=range(1,51)\nfig,(ax1,ax2)=plt.subplots(1,2,figsize=(15,5))\nax1.plot(epochs,loss_values,'bo',label='Training Loss')\nax1.plot(epochs,val_loss_values,'orange', label='Validation Loss')\nax1.set_title('Training and validation loss')\nax1.set_xlabel('Epochs')\nax1.set_ylabel('Loss')\nax1.legend()\nax2.plot(epochs,acc_values,'bo', label='Training accuracy')\nax2.plot(epochs,val_acc_values,'orange',label='Validation accuracy')\nax2.set_title('Training and validation accuracy')\nax2.set_xlabel('Epochs')\nax2.set_ylabel('Accuracy')\nax2.legend()\nplt.show()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_loss, test_acc = CNNmodel.evaluate(X_test, y_test)\nprint('Test loss:', test_loss)\nprint('Test accuracy:', test_acc)","metadata":{"execution":{"iopub.status.busy":"2023-04-12T07:11:56.157044Z","iopub.execute_input":"2023-04-12T07:11:56.15779Z","iopub.status.idle":"2023-04-12T07:11:56.240312Z","shell.execute_reply.started":"2023-04-12T07:11:56.157752Z","shell.execute_reply":"2023-04-12T07:11:56.238639Z"},"trusted":true},"execution_count":1,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m/tmp/ipykernel_23/3350755243.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtest_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_acc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCNNmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Test loss:'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_loss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Test accuracy:'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_acc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'CNNmodel' is not defined"],"ename":"NameError","evalue":"name 'CNNmodel' is not defined","output_type":"error"}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}