diff --git a/Smile Recognition with Deep Learning/Dataset/Readme.md b/Smile Recognition with Deep Learning/Dataset/Readme.md new file mode 100644 index 000000000..cd2c952f4 --- /dev/null +++ b/Smile Recognition with Deep Learning/Dataset/Readme.md @@ -0,0 +1,2 @@ +https://www.kaggle.com/datasets/chazzer/smiling-or-not-face-data +Dataset \ No newline at end of file diff --git a/Smile Recognition with Deep Learning/Images/Readme.md b/Smile Recognition with Deep Learning/Images/Readme.md new file mode 100644 index 000000000..8a911ab5e --- /dev/null +++ b/Smile Recognition with Deep Learning/Images/Readme.md @@ -0,0 +1 @@ +Added EDA for visualizing performance of neural networks diff --git a/Smile Recognition with Deep Learning/Images/Screenshot (372).png b/Smile Recognition with Deep Learning/Images/Screenshot (372).png new file mode 100644 index 000000000..08c93d34d Binary files /dev/null and b/Smile Recognition with Deep Learning/Images/Screenshot (372).png differ diff --git a/Smile Recognition with Deep Learning/Images/cnn.png b/Smile Recognition with Deep Learning/Images/cnn.png new file mode 100644 index 000000000..35c5ecf31 Binary files /dev/null and b/Smile Recognition with Deep Learning/Images/cnn.png differ diff --git a/Smile Recognition with Deep Learning/Images/densenet.png b/Smile Recognition with Deep Learning/Images/densenet.png new file mode 100644 index 000000000..622ae40c2 Binary files /dev/null and b/Smile Recognition with Deep Learning/Images/densenet.png differ diff --git a/Smile Recognition with Deep Learning/Images/test.png b/Smile Recognition with Deep Learning/Images/test.png new file mode 100644 index 000000000..b7d150160 Binary files /dev/null and b/Smile Recognition with Deep Learning/Images/test.png differ diff --git a/Smile Recognition with Deep Learning/Images/traing.png b/Smile Recognition with Deep Learning/Images/traing.png new file mode 100644 index 000000000..f207cf138 Binary files /dev/null and b/Smile Recognition with Deep Learning/Images/traing.png differ diff --git a/Smile Recognition with Deep Learning/Models/Readme.md b/Smile Recognition with Deep Learning/Models/Readme.md new file mode 100644 index 000000000..d0a273fcf --- /dev/null +++ b/Smile Recognition with Deep Learning/Models/Readme.md @@ -0,0 +1,38 @@ +**PROJECT TITLE** +Smile Recognition with Deep Learning + +**GOAL** +Smile Recognition with Deep Learning + +**DATASET** +https://www.kaggle.com/datasets/chazzer/smiling-or-not-face-data + +**DESCRIPTION** +The project uses CNN model and DenseNet model. And then we compare performance of our 2 models in recognizing smiling faces and separating between a smiling and a non-smiling face + +**WHAT I HAD DONE** +1. Created a dataset from the pictures, which led us to discover an almost perfectly balanced dataset with pictures of both categories in 1:1 ratio. +2. The data streams are created for training, validation, and testing datasets. These data streams will generate batches of data for each iteration.The function returns three generator objects (train_ds, valid_ds, and test_ds) which can be used to iterate over the data streams. +3. We create a function,in which we use data generators to perform image data augmentation and apply the MyDenseNet model. +4. Then we create the Densenet neural network model and observe accuracy. We plot charts for accuracy and value loss +5. We also create a CNN model to do the same tasks + + +**MODELS USED** +CNN, Densenet + +**LIBRARIES NEEDED** +Pandas, Numpy, Keras,TensorFlow, ScikitLearn, Seaborn, Matplotlib + +**VISUALIZATION** +![Alt text](../Images/cnn.png) - PERFORMANCE OF CNN +![Alt text](../Images/densenet.png) - PERFORMANCE OF DENSENET + +**ACCURACIES** +Standard CNNs over 98% accuracy as against 95% accuracy for DenseNet Model used + +**CONCLUSION** + +While DenseNet and traditional Convolutional Neural Networks (CNNs) are both effective for recognizing smiling faces, CNNs often provide higher accuracy due to their ability to capture hierarchical features through convolutional layers. DenseNet's dense connectivity may result in more parameters and computations, making it potentially more resource-intensive. In smile recognition tasks where nuanced facial features matter, CNNs excel by efficiently learning hierarchical representations, leading to superior accuracy compared to DenseNet. This explains CNN's 98% over the 95% of DenseNet +**YOUR NAME** +Aindree Chatterjee \ No newline at end of file diff --git a/Smile Recognition with Deep Learning/Models/cnn-smile.ipynb b/Smile Recognition with Deep Learning/Models/cnn-smile.ipynb new file mode 100644 index 000000000..6492cfea0 --- /dev/null +++ b/Smile Recognition with Deep Learning/Models/cnn-smile.ipynb @@ -0,0 +1 @@ +{"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"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":4193505,"sourceType":"datasetVersion","datasetId":2473086}],"dockerImageVersionId":30302,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import glob\nfrom PIL import Image\nimport numpy as np\nimport pandas as pd","metadata":{"execution":{"iopub.status.busy":"2023-12-24T17:56:02.333165Z","iopub.execute_input":"2023-12-24T17:56:02.333583Z","iopub.status.idle":"2023-12-24T17:56:02.367245Z","shell.execute_reply.started":"2023-12-24T17:56:02.333504Z","shell.execute_reply":"2023-12-24T17:56:02.366250Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"no_smile = glob.glob(\"../input/smiling-or-not-face-data/non_smile/*.jpg\")\nsmile = glob.glob(\"../input/smiling-or-not-face-data/smile/*.jpg\")\ntest = glob.glob(\"../input/smiling-or-not-face-data/test/*.jpg\")","metadata":{"execution":{"iopub.status.busy":"2023-12-24T17:56:02.369079Z","iopub.execute_input":"2023-12-24T17:56:02.369552Z","iopub.status.idle":"2023-12-24T17:56:03.291910Z","shell.execute_reply.started":"2023-12-24T17:56:02.369514Z","shell.execute_reply":"2023-12-24T17:56:03.290857Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"X=[]\ny=[]\ntest_data = []\nfor i in no_smile:\n X_i = Image.open(i)\n X_i = np.array(X_i.resize((32,32)))/255\n X.append(X_i)\n y.append(\"no smile\")\n \nfor i in smile:\n X_i = Image.open(i)\n X_i = np.array(X_i.resize((32,32)))/255\n X.append(X_i)\n y.append(\"smile\")\n \nfor i in test:\n X_i = Image.open(i)\n X_i = np.array(X_i.resize((32,32)))/255\n test_data.append(X_i)","metadata":{"execution":{"iopub.status.busy":"2023-12-24T17:56:03.293235Z","iopub.execute_input":"2023-12-24T17:56:03.293834Z","iopub.status.idle":"2023-12-24T17:57:20.128133Z","shell.execute_reply.started":"2023-12-24T17:56:03.293804Z","shell.execute_reply":"2023-12-24T17:57:20.127258Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"X = np.array(X)\ny = np.array(y)\ntest = np.array(test)\nfrom sklearn.utils import shuffle\nX,y = shuffle(X,y,random_state=0)","metadata":{"execution":{"iopub.status.busy":"2023-12-24T17:57:20.130474Z","iopub.execute_input":"2023-12-24T17:57:20.130811Z","iopub.status.idle":"2023-12-24T17:57:20.621449Z","shell.execute_reply.started":"2023-12-24T17:57:20.130780Z","shell.execute_reply":"2023-12-24T17:57:20.620262Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"from sklearn.preprocessing import LabelEncoder\nle = LabelEncoder()\ny = le.fit_transform(y)","metadata":{"execution":{"iopub.status.busy":"2023-12-24T17:57:20.622883Z","iopub.execute_input":"2023-12-24T17:57:20.623328Z","iopub.status.idle":"2023-12-24T17:57:20.658191Z","shell.execute_reply.started":"2023-12-24T17:57:20.623284Z","shell.execute_reply":"2023-12-24T17:57:20.657177Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nX_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3,random_state=45)","metadata":{"execution":{"iopub.status.busy":"2023-12-24T17:57:20.659573Z","iopub.execute_input":"2023-12-24T17:57:20.660027Z","iopub.status.idle":"2023-12-24T17:57:20.735433Z","shell.execute_reply.started":"2023-12-24T17:57:20.659964Z","shell.execute_reply":"2023-12-24T17:57:20.734291Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"#X_train = X_train.reshape(962,32,32,3)\n#X_train = X_train.astype(\"float32\")/255\n#X_test = X_test.reshape(241,32,32,3)\n#X_test = X_test.astype(\"float32\")/255","metadata":{"execution":{"iopub.status.busy":"2023-12-24T17:57:20.736779Z","iopub.execute_input":"2023-12-24T17:57:20.737082Z","iopub.status.idle":"2023-12-24T17:57:20.741856Z","shell.execute_reply.started":"2023-12-24T17:57:20.737055Z","shell.execute_reply":"2023-12-24T17:57:20.740824Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"from tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Conv2D,Dense,Flatten,MaxPooling2D,Dropout,BatchNormalization\nfrom tensorflow.keras.callbacks import EarlyStopping","metadata":{"execution":{"iopub.status.busy":"2023-12-24T17:57:20.743418Z","iopub.execute_input":"2023-12-24T17:57:20.743955Z","iopub.status.idle":"2023-12-24T17:57:27.088141Z","shell.execute_reply.started":"2023-12-24T17:57:20.743901Z","shell.execute_reply":"2023-12-24T17:57:27.087295Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"callback = EarlyStopping(patience=60)","metadata":{"execution":{"iopub.status.busy":"2023-12-24T17:57:27.089248Z","iopub.execute_input":"2023-12-24T17:57:27.089744Z","iopub.status.idle":"2023-12-24T17:57:27.094669Z","shell.execute_reply.started":"2023-12-24T17:57:27.089717Z","shell.execute_reply":"2023-12-24T17:57:27.093532Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"model = Sequential([Conv2D(128,(3,3),activation=\"relu\",input_shape= X_train.shape[1:]),\n MaxPooling2D((2,2)),\n Dropout(0.3),\n BatchNormalization(),\n Conv2D(64,(3,3),activation=\"relu\"),\n MaxPooling2D((2,2)),\n Conv2D(64,(3,3),activation=\"relu\"),\n Flatten(),\n Dense(128,activation=\"relu\"),\n Dropout(0.1),\n BatchNormalization(),\n Dense(128,activation=\"relu\"),\n Dense(1,activation=\"sigmoid\")])\nmodel.compile(optimizer=\"adam\",loss=\"binary_crossentropy\",metrics=[\"accuracy\"])\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2023-12-24T17:57:27.098380Z","iopub.execute_input":"2023-12-24T17:57:27.098982Z","iopub.status.idle":"2023-12-24T17:57:31.033981Z","shell.execute_reply.started":"2023-12-24T17:57:27.098937Z","shell.execute_reply":"2023-12-24T17:57:31.033052Z"},"trusted":true},"execution_count":10,"outputs":[{"name":"stdout","text":"Model: \"sequential\"\n_________________________________________________________________\nLayer (type) Output Shape Param # \n=================================================================\nconv2d (Conv2D) (None, 30, 30, 128) 3584 \n_________________________________________________________________\nmax_pooling2d (MaxPooling2D) (None, 15, 15, 128) 0 \n_________________________________________________________________\ndropout (Dropout) (None, 15, 15, 128) 0 \n_________________________________________________________________\nbatch_normalization (BatchNo (None, 15, 15, 128) 512 \n_________________________________________________________________\nconv2d_1 (Conv2D) (None, 13, 13, 64) 73792 \n_________________________________________________________________\nmax_pooling2d_1 (MaxPooling2 (None, 6, 6, 64) 0 \n_________________________________________________________________\nconv2d_2 (Conv2D) (None, 4, 4, 64) 36928 \n_________________________________________________________________\nflatten (Flatten) (None, 1024) 0 \n_________________________________________________________________\ndense (Dense) (None, 128) 131200 \n_________________________________________________________________\ndropout_1 (Dropout) (None, 128) 0 \n_________________________________________________________________\nbatch_normalization_1 (Batch (None, 128) 512 \n_________________________________________________________________\ndense_1 (Dense) (None, 128) 16512 \n_________________________________________________________________\ndense_2 (Dense) (None, 1) 129 \n=================================================================\nTotal params: 263,169\nTrainable params: 262,657\nNon-trainable params: 512\n_________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"code","source":"train_hist = model.fit(X_train,y_train,epochs=80,batch_size=8,callbacks=[callback],validation_split=0.1)","metadata":{"execution":{"iopub.status.busy":"2023-12-24T17:57:31.035046Z","iopub.execute_input":"2023-12-24T17:57:31.035324Z","iopub.status.idle":"2023-12-24T17:58:16.867817Z","shell.execute_reply.started":"2023-12-24T17:57:31.035298Z","shell.execute_reply":"2023-12-24T17:58:16.866808Z"},"trusted":true},"execution_count":11,"outputs":[{"name":"stdout","text":"Epoch 1/80\n95/95 [==============================] - 11s 8ms/step - loss: 0.5609 - accuracy: 0.7001 - val_loss: 0.6884 - val_accuracy: 0.4941\nEpoch 2/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.3824 - accuracy: 0.8375 - val_loss: 0.8130 - val_accuracy: 0.4941\nEpoch 3/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.3551 - accuracy: 0.8520 - val_loss: 0.7875 - val_accuracy: 0.5059\nEpoch 4/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.3338 - accuracy: 0.8666 - val_loss: 1.2650 - val_accuracy: 0.4941\nEpoch 5/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.2838 - accuracy: 0.8890 - val_loss: 1.1086 - val_accuracy: 0.5176\nEpoch 6/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.2364 - accuracy: 0.9022 - val_loss: 1.2169 - val_accuracy: 0.4941\nEpoch 7/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.2806 - accuracy: 0.8838 - val_loss: 0.3021 - val_accuracy: 0.9176\nEpoch 8/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.2319 - accuracy: 0.9049 - val_loss: 0.3250 - val_accuracy: 0.8706\nEpoch 9/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.2818 - accuracy: 0.8838 - val_loss: 0.1505 - val_accuracy: 0.9529\nEpoch 10/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.2405 - accuracy: 0.9102 - val_loss: 0.2638 - val_accuracy: 0.9059\nEpoch 11/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.2736 - accuracy: 0.8996 - val_loss: 0.5206 - val_accuracy: 0.7412\nEpoch 12/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.2280 - accuracy: 0.9194 - val_loss: 0.1916 - val_accuracy: 0.9412\nEpoch 13/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.1905 - accuracy: 0.9287 - val_loss: 0.4410 - val_accuracy: 0.8000\nEpoch 14/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.1707 - accuracy: 0.9339 - val_loss: 0.1543 - val_accuracy: 0.9529\nEpoch 15/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.2035 - accuracy: 0.9194 - val_loss: 0.2603 - val_accuracy: 0.8471\nEpoch 16/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.2547 - accuracy: 0.9075 - val_loss: 0.1143 - val_accuracy: 0.9765\nEpoch 17/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.1864 - accuracy: 0.9287 - val_loss: 0.2357 - val_accuracy: 0.8941\nEpoch 18/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.1592 - accuracy: 0.9564 - val_loss: 0.1810 - val_accuracy: 0.9412\nEpoch 19/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.1663 - accuracy: 0.9406 - val_loss: 0.1101 - val_accuracy: 0.9529\nEpoch 20/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.1567 - accuracy: 0.9392 - val_loss: 0.1865 - val_accuracy: 0.9412\nEpoch 21/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.2063 - accuracy: 0.9260 - val_loss: 0.1229 - val_accuracy: 0.9647\nEpoch 22/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.1880 - accuracy: 0.9287 - val_loss: 0.1048 - val_accuracy: 0.9765\nEpoch 23/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.1636 - accuracy: 0.9419 - val_loss: 0.0918 - val_accuracy: 0.9647\nEpoch 24/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.1937 - accuracy: 0.9392 - val_loss: 0.3468 - val_accuracy: 0.8824\nEpoch 25/80\n95/95 [==============================] - 1s 6ms/step - loss: 0.1563 - accuracy: 0.9472 - val_loss: 0.1097 - val_accuracy: 0.9765\nEpoch 26/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.1766 - accuracy: 0.9247 - val_loss: 0.1489 - val_accuracy: 0.9647\nEpoch 27/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.1828 - accuracy: 0.9313 - val_loss: 0.1695 - val_accuracy: 0.9412\nEpoch 28/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.1455 - accuracy: 0.9445 - val_loss: 0.0892 - val_accuracy: 0.9765\nEpoch 29/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.1090 - accuracy: 0.9551 - val_loss: 0.1478 - val_accuracy: 0.9059\nEpoch 30/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.1671 - accuracy: 0.9472 - val_loss: 0.1379 - val_accuracy: 0.9529\nEpoch 31/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.1243 - accuracy: 0.9577 - val_loss: 0.0820 - val_accuracy: 0.9882\nEpoch 32/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.1088 - accuracy: 0.9617 - val_loss: 0.1033 - val_accuracy: 0.9765\nEpoch 33/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.1208 - accuracy: 0.9577 - val_loss: 0.1175 - val_accuracy: 0.9529\nEpoch 34/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.1160 - accuracy: 0.9577 - val_loss: 0.1042 - val_accuracy: 0.9647\nEpoch 35/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.0890 - accuracy: 0.9683 - val_loss: 0.0771 - val_accuracy: 0.9765\nEpoch 36/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.0873 - accuracy: 0.9736 - val_loss: 0.1247 - val_accuracy: 0.9647\nEpoch 37/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.0689 - accuracy: 0.9736 - val_loss: 0.1624 - val_accuracy: 0.9529\nEpoch 38/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.1002 - accuracy: 0.9683 - val_loss: 0.1423 - val_accuracy: 0.9765\nEpoch 39/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.0752 - accuracy: 0.9723 - val_loss: 0.1764 - val_accuracy: 0.9529\nEpoch 40/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.1302 - accuracy: 0.9590 - val_loss: 0.1014 - val_accuracy: 0.9765\nEpoch 41/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.1059 - accuracy: 0.9643 - val_loss: 0.1071 - val_accuracy: 0.9647\nEpoch 42/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.1073 - accuracy: 0.9577 - val_loss: 0.0945 - val_accuracy: 0.9765\nEpoch 43/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.0689 - accuracy: 0.9723 - val_loss: 0.1209 - val_accuracy: 0.9647\nEpoch 44/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.0640 - accuracy: 0.9815 - val_loss: 0.0736 - val_accuracy: 0.9765\nEpoch 45/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.1355 - accuracy: 0.9551 - val_loss: 0.0957 - val_accuracy: 0.9647\nEpoch 46/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.1008 - accuracy: 0.9643 - val_loss: 0.0993 - val_accuracy: 0.9765\nEpoch 47/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.0561 - accuracy: 0.9802 - val_loss: 0.0942 - val_accuracy: 0.9765\nEpoch 48/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.0775 - accuracy: 0.9749 - val_loss: 0.1451 - val_accuracy: 0.9647\nEpoch 49/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.1395 - accuracy: 0.9498 - val_loss: 0.2276 - val_accuracy: 0.9176\nEpoch 50/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.1300 - accuracy: 0.9604 - val_loss: 0.0952 - val_accuracy: 0.9765\nEpoch 51/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.0938 - accuracy: 0.9709 - val_loss: 0.0690 - val_accuracy: 0.9882\nEpoch 52/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.0972 - accuracy: 0.9736 - val_loss: 0.0958 - val_accuracy: 0.9765\nEpoch 53/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.0829 - accuracy: 0.9670 - val_loss: 0.1154 - val_accuracy: 0.9647\nEpoch 54/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.1176 - accuracy: 0.9524 - val_loss: 0.0976 - val_accuracy: 0.9765\nEpoch 55/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.1371 - accuracy: 0.9524 - val_loss: 0.0989 - val_accuracy: 0.9765\nEpoch 56/80\n95/95 [==============================] - 0s 4ms/step - loss: 0.0879 - accuracy: 0.9696 - val_loss: 0.0979 - val_accuracy: 0.9765\nEpoch 57/80\n95/95 [==============================] - 0s 4ms/step - loss: 0.0517 - accuracy: 0.9868 - val_loss: 0.1320 - val_accuracy: 0.9529\nEpoch 58/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.0380 - accuracy: 0.9894 - val_loss: 0.1154 - val_accuracy: 0.9647\nEpoch 59/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.0815 - accuracy: 0.9736 - val_loss: 0.0757 - val_accuracy: 0.9882\nEpoch 60/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.0561 - accuracy: 0.9841 - val_loss: 0.1017 - val_accuracy: 0.9647\nEpoch 61/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.0373 - accuracy: 0.9855 - val_loss: 0.0720 - val_accuracy: 0.9765\nEpoch 62/80\n95/95 [==============================] - 0s 4ms/step - loss: 0.0952 - accuracy: 0.9670 - val_loss: 0.3028 - val_accuracy: 0.9059\nEpoch 63/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.1160 - accuracy: 0.9683 - val_loss: 0.2016 - val_accuracy: 0.9059\nEpoch 64/80\n95/95 [==============================] - 0s 4ms/step - loss: 0.1007 - accuracy: 0.9657 - val_loss: 0.1233 - val_accuracy: 0.9647\nEpoch 65/80\n95/95 [==============================] - 0s 4ms/step - loss: 0.0945 - accuracy: 0.9683 - val_loss: 0.2249 - val_accuracy: 0.9412\nEpoch 66/80\n95/95 [==============================] - 0s 4ms/step - loss: 0.0676 - accuracy: 0.9736 - val_loss: 0.0887 - val_accuracy: 0.9882\nEpoch 67/80\n95/95 [==============================] - 0s 4ms/step - loss: 0.0945 - accuracy: 0.9723 - val_loss: 0.1335 - val_accuracy: 0.9765\nEpoch 68/80\n95/95 [==============================] - 0s 4ms/step - loss: 0.0331 - accuracy: 0.9881 - val_loss: 0.0932 - val_accuracy: 0.9882\nEpoch 69/80\n95/95 [==============================] - 0s 4ms/step - loss: 0.0448 - accuracy: 0.9855 - val_loss: 0.0771 - val_accuracy: 0.9765\nEpoch 70/80\n95/95 [==============================] - 0s 4ms/step - loss: 0.0440 - accuracy: 0.9894 - val_loss: 0.0989 - val_accuracy: 0.9647\nEpoch 71/80\n95/95 [==============================] - 0s 4ms/step - loss: 0.0525 - accuracy: 0.9815 - val_loss: 0.6357 - val_accuracy: 0.8824\nEpoch 72/80\n95/95 [==============================] - 0s 4ms/step - loss: 0.1175 - accuracy: 0.9590 - val_loss: 0.7027 - val_accuracy: 0.7765\nEpoch 73/80\n95/95 [==============================] - 0s 4ms/step - loss: 0.2686 - accuracy: 0.8970 - val_loss: 0.1268 - val_accuracy: 0.9647\nEpoch 74/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.1721 - accuracy: 0.9406 - val_loss: 0.1573 - val_accuracy: 0.9765\nEpoch 75/80\n95/95 [==============================] - 0s 4ms/step - loss: 0.1354 - accuracy: 0.9524 - val_loss: 0.3331 - val_accuracy: 0.8588\nEpoch 76/80\n95/95 [==============================] - 0s 4ms/step - loss: 0.0881 - accuracy: 0.9630 - val_loss: 0.0920 - val_accuracy: 0.9765\nEpoch 77/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.0957 - accuracy: 0.9643 - val_loss: 0.1704 - val_accuracy: 0.9529\nEpoch 78/80\n95/95 [==============================] - 0s 5ms/step - loss: 0.0774 - accuracy: 0.9762 - val_loss: 0.1014 - val_accuracy: 0.9647\nEpoch 79/80\n95/95 [==============================] - 0s 4ms/step - loss: 0.0499 - accuracy: 0.9828 - val_loss: 0.0972 - val_accuracy: 0.9765\nEpoch 80/80\n95/95 [==============================] - 0s 4ms/step - loss: 0.0375 - accuracy: 0.9868 - val_loss: 0.0898 - val_accuracy: 0.9765\n","output_type":"stream"}]},{"cell_type":"code","source":"import matplotlib.pyplot as plt","metadata":{"execution":{"iopub.status.busy":"2023-12-24T17:59:01.163032Z","iopub.execute_input":"2023-12-24T17:59:01.163956Z","iopub.status.idle":"2023-12-24T17:59:01.168158Z","shell.execute_reply.started":"2023-12-24T17:59:01.163916Z","shell.execute_reply":"2023-12-24T17:59:01.167122Z"},"trusted":true},"execution_count":14,"outputs":[]},{"cell_type":"code","source":"plt.plot(train_hist.history['val_loss'])\nplt.title(\"loss\")\nplt.show()\nplt.plot(train_hist.history['val_accuracy'])\nplt.title(\"accuracy\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-12-24T17:59:44.143584Z","iopub.execute_input":"2023-12-24T17:59:44.143984Z","iopub.status.idle":"2023-12-24T17:59:44.548205Z","shell.execute_reply.started":"2023-12-24T17:59:44.143952Z","shell.execute_reply":"2023-12-24T17:59:44.547306Z"},"trusted":true},"execution_count":16,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]}]} \ No newline at end of file diff --git a/Smile Recognition with Deep Learning/Models/densenet.ipynb b/Smile Recognition with Deep Learning/Models/densenet.ipynb new file mode 100644 index 000000000..b70a0b531 --- /dev/null +++ b/Smile Recognition with Deep Learning/Models/densenet.ipynb @@ -0,0 +1 @@ +{"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"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":4193505,"sourceType":"datasetVersion","datasetId":2473086}],"dockerImageVersionId":30236,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"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 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ing-or-not-face-data/test/Valentino_Rossi_0005.jpg\n/kaggle/input/smiling-or-not-face-data/test/Itamar_Franco_0001.jpg\n/kaggle/input/smiling-or-not-face-data/test/Abdullah_0002.jpg\n/kaggle/input/smiling-or-not-face-data/test/Luis_Pujols_0001.jpg\n/kaggle/input/smiling-or-not-face-data/test/Vanessa_Redgrave_0004.jpg\n/kaggle/input/smiling-or-not-face-data/test/Hans_Blix_0029.jpg\n/kaggle/input/smiling-or-not-face-data/test/Alan_Greenspan_0003.jpg\n/kaggle/input/smiling-or-not-face-data/test/Linda_Franklin_0001.jpg\n/kaggle/input/smiling-or-not-face-data/test/George_W_Bush_0519.jpg\n/kaggle/input/smiling-or-not-face-data/test/Roman_Polanski_0002.jpg\n/kaggle/input/smiling-or-not-face-data/test/Colin_Powell_0104.jpg\n/kaggle/input/smiling-or-not-face-data/test/Nathan_Doudney_0001.jpg\n/kaggle/input/smiling-or-not-face-data/test/Saoud_Al_Faisal_0001.jpg\n/kaggle/input/smiling-or-not-face-data/test/Laura_Linney_0004.jpg\n/kaggle/input/smiling-or-not-face-data/test/Maureen_Kanka_0001.jpg\n/kaggle/input/smiling-or-not-face-data/test/Mahathir_Mohamad_0013.jpg\n/kaggle/input/smiling-or-not-face-data/test/Donald_Rumsfeld_0098.jpg\n/kaggle/input/smiling-or-not-face-data/test/Keith_Olbermann_0001.jpg\n","output_type":"stream"}]},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport os, shutil, math, cv2\nfrom tqdm import tqdm\nfrom keras.layers import *\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.metrics import accuracy_score, precision_recall_fscore_support, f1_score, classification_report, confusion_matrix\nfrom sklearn.model_selection import train_test_split\nimport tensorflow as tf\nfrom keras.preprocessing.image import ImageDataGenerator","metadata":{"execution":{"iopub.status.busy":"2023-12-26T13:27:42.423152Z","iopub.execute_input":"2023-12-26T13:27:42.423475Z","iopub.status.idle":"2023-12-26T13:27:48.406026Z","shell.execute_reply.started":"2023-12-26T13:27:42.423444Z","shell.execute_reply":"2023-12-26T13:27:48.405163Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"sdir = '/kaggle/input/smiling-or-not-face-data/'\n\ndef make_dataframes(sdir):\n bad_images = []\n good_ext = ['jpg', 'jpeg', 'png', 'tiff']\n filepaths = []\n labels = []\n classes = sorted(['smile', 'non_smile'])\n for klass in classes:\n classpath = os.path.join(sdir, klass)\n flist = sorted(os.listdir(classpath))\n desc = f'{klass:23s}'\n for f in tqdm(flist, ncols=110, desc=desc, unit='file', colour='blue'):\n fpath = os.path.join(classpath, f)\n fl = f.lower()\n index = fl.rfind('.')\n ext = fl[index + 1:]\n if ext in good_ext:\n try:\n img = cv2.imread(fpath)\n shape = img.shape\n filepaths.append(fpath)\n labels.append(klass)\n except:\n bad_images.append(fpath)\n print('defective image file: ', fpath)\n else:\n bad_images.append(fpath)\n Fseries = pd.Series(filepaths, name='filepaths')\n Lseries = pd.Series(labels, name='labels')\n df = pd.concat([Fseries, Lseries], axis=1)\n\n train_df, dummy_df = train_test_split(df, train_size=.8, shuffle=True, random_state=123, stratify=df['labels'])\n valid_df, test_df = train_test_split(dummy_df, train_size=.5, shuffle=True, random_state=123,\n stratify=dummy_df['labels'])\n classes = sorted(train_df['labels'].unique())\n class_count = len(classes)\n sample_df = train_df.sample(n=50, replace=False)\n\n ht = 0\n wt = 0\n count = 0\n for i in range(len(sample_df)):\n fpath = sample_df['filepaths'].iloc[i]\n try:\n img = cv2.imread(fpath)\n h = img.shape[0]\n w = img.shape[1]\n wt += w\n ht += h\n count += 1\n except:\n pass\n have = int(ht / count)\n wave = int(wt / count)\n aspect_ratio = have / wave\n print('number of classes in processed dataset= ', class_count)\n counts = list(train_df['labels'].value_counts())\n print('the maximum files in any class in train_df is ', max(counts),\n ' the minimum files in any class in train_df is ', min(counts))\n print('train_df length: ', len(train_df), ' test_df length: ', len(test_df), ' valid_df length: ', len(valid_df))\n print('average image height= ', have, ' average image width= ', wave, ' aspect ratio h/w= ', aspect_ratio)\n return train_df, test_df, valid_df, classes, class_count\n\n\ntrain_df, test_df, valid_df, classes, class_count = make_dataframes(sdir)","metadata":{"execution":{"iopub.status.busy":"2023-12-26T13:27:48.407201Z","iopub.execute_input":"2023-12-26T13:27:48.407779Z","iopub.status.idle":"2023-12-26T13:27:55.083930Z","shell.execute_reply.started":"2023-12-26T13:27:48.407747Z","shell.execute_reply":"2023-12-26T13:27:55.082873Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stderr","text":"non_smile : 100%|\u001b[34m███████████████████████████████████████████\u001b[0m| 603/603 [00:03<00:00, 180.18file/s]\u001b[0m\nsmile : 100%|\u001b[34m███████████████████████████████████████████\u001b[0m| 600/600 [00:03<00:00, 185.83file/s]\u001b[0m","output_type":"stream"},{"name":"stdout","text":"number of classes in processed dataset= 2\nthe maximum files in any class in train_df is 482 the minimum files in any class in train_df is 480\ntrain_df length: 962 test_df length: 121 valid_df length: 120\naverage image height= 64 average image width= 64 aspect ratio h/w= 1.0\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"}]},{"cell_type":"code","source":"category_grouped = test_df.groupby('labels')\ncategory_counts = category_grouped.size()\nimport matplotlib.pyplot as plt\nplt.figure(figsize=(10, 6))\ncategory_counts.plot(kind='bar', color='skyblue')\nplt.title('Count of Occurrences in Each Category- Test')\nplt.xlabel('Category')\nplt.ylabel('Count')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-12-26T13:27:55.087351Z","iopub.execute_input":"2023-12-26T13:27:55.087875Z","iopub.status.idle":"2023-12-26T13:27:55.265789Z","shell.execute_reply.started":"2023-12-26T13:27:55.087831Z","shell.execute_reply":"2023-12-26T13:27:55.264791Z"},"trusted":true},"execution_count":4,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"epochs = 30\nbatch_size = 16\nimg_size = (64, 64)\ninput_shape = (64, 64, 3)","metadata":{"execution":{"iopub.status.busy":"2023-12-26T13:27:55.267107Z","iopub.execute_input":"2023-12-26T13:27:55.267488Z","iopub.status.idle":"2023-12-26T13:27:55.275298Z","shell.execute_reply.started":"2023-12-26T13:27:55.267450Z","shell.execute_reply":"2023-12-26T13:27:55.274434Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"def make_gens(batch_size, train_df, test_df, valid_df, img_size):\n trgen = ImageDataGenerator(horizontal_flip=True)\n t_and_v_gen = ImageDataGenerator()\n msg = '{0:70s} for train generator'.format(' ')\n print(msg, '\\r', end='')\n train_ds = trgen.flow_from_dataframe(train_df, x_col='filepaths', y_col='labels',\n target_size=img_size, class_mode='categorical',\n color_mode='rgb', batch_size=batch_size, shuffle=True)\n\n msg = '{0:70s} for valid generator'.format(' ')\n print(msg, '\\r', end='')\n valid_ds = t_and_v_gen.flow_from_dataframe(valid_df, x_col='filepaths', y_col='labels',\n target_size=img_size, class_mode='categorical',\n color_mode='rgb', batch_size=batch_size, shuffle=False)\n\n test_len = len(test_df)\n test_batch_size = sorted([int(test_len / n) for n in range(1, test_len + 1)\n if test_len % n == 0 and test_len / n<=80], reverse=True)[0]\n test_steps = int(test_len / test_batch_size)\n msg = '{0:70s} for test generator'.format(' ')\n print(msg, '\\r', end='')\n test_ds = t_and_v_gen.flow_from_dataframe(test_df, x_col='filepaths', y_col='labels',\n target_size=img_size, class_mode='categorical',\n color_mode='rgb', batch_size=batch_size, shuffle=False)\n\n classes = list(train_ds.class_indices.keys())\n class_count = len(classes)\n print('test batch size: ', test_batch_size, 'test steps: ', test_steps, 'number of classes : ', class_count)\n\n return train_ds, test_ds, valid_ds\n\ntrain_ds, test_ds, valid_ds = make_gens(batch_size, train_df, test_df, valid_df, img_size)","metadata":{"execution":{"iopub.status.busy":"2023-12-26T13:27:55.276664Z","iopub.execute_input":"2023-12-26T13:27:55.277486Z","iopub.status.idle":"2023-12-26T13:27:55.745604Z","shell.execute_reply.started":"2023-12-26T13:27:55.277446Z","shell.execute_reply":"2023-12-26T13:27:55.744536Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stdout","text":"Found 962 validated image filenames belonging to 2 classes. for train generator \nFound 120 validated image filenames belonging to 2 classes. for valid generator \nFound 121 validated image filenames belonging to 2 classes. for test generator \ntest batch size: 11 test steps: 11 number of classes : 2\n","output_type":"stream"}]},{"cell_type":"code","source":"class BottleNeck(tf.keras.layers.Layer):\n def __init__(self, growth_rate, drop_rate):\n super(BottleNeck, self).__init__()\n self.B1 = BatchNormalization()\n self.A1 = Activation('relu')\n self.C1 = Conv2D(filters=4 * growth_rate, kernel_size=(1, 1), strides=1, padding='same')\n self.B2 = BatchNormalization()\n self.A2 = Activation('relu')\n self.C2 = Conv2D(filters=growth_rate, kernel_size=(3, 3), strides=1, padding='same')\n self.Dr1 = Dropout(drop_rate)\n\n def call(self, inputs, training=None, **kwargs):\n x = self.B1(inputs, training=training)\n x = self.A1(x)\n x = self.C1(x)\n x = self.B2(x, training=training)\n x = self.A2(x)\n x = self.C2(x)\n y = self.Dr1(x, training=training)\n return y\n\n\nclass DenseBlock(tf.keras.layers.Layer):\n def __init__(self, num_layers, growth_rate, drop_rate):\n super(DenseBlock, self).__init__()\n self.num_layers = num_layers\n self.growth_rate = growth_rate\n self.drop_rate = drop_rate\n self.features_list = []\n self.bottle_necks = []\n for i in range(self.num_layers):\n self.bottle_necks.append(\n BottleNeck(growth_rate=self.growth_rate, drop_rate=self.drop_rate)\n )\n\n def call(self, inputs, training=None, **kwargs):\n self.features_list.append(inputs)\n x = inputs\n for i in range(self.num_layers):\n y = self.bottle_necks[i](x, training=training)\n self.features_list.append(y)\n x = tf.concat(self.features_list, axis=-1)\n self.features_list.clear()\n return x\n\n\nclass TransitionLayer(tf.keras.layers.Layer):\n def __init__(self, out_channels):\n super(TransitionLayer, self).__init__()\n self.B1 = BatchNormalization()\n self.A1 = Activation('relu')\n self.C1 = Conv2D(filters=out_channels, kernel_size=(1, 1), strides=1, padding='same')\n self.P1 = MaxPooling2D(2, padding='same')\n\n def call(self, inputs, training=None, **kwargs):\n x = self.B1(inputs, training=training)\n x = self.A1(x)\n x = self.C1(x)\n y = self.P1(x)\n return y\n\n\nclass MyDenseNet(tf.keras.layers.Layer):\n def __init__(self, num_init_features, growth_rate, block_layers, compression_rate, drop_rate):\n super(MyDenseNet, self).__init__()\n self.C1 = Conv2D(filters=num_init_features, kernel_size=(7, 7), strides=2, padding=\"same\", input_shape=input_shape)\n self.B1 = BatchNormalization()\n self.A1 = Activation('relu')\n self.P1 = MaxPooling2D(pool_size=(3, 3), strides=2, padding=\"same\")\n\n self.num_channels = num_init_features\n self.dense_block_1 = DenseBlock(num_layers=block_layers[0], growth_rate=growth_rate, drop_rate=drop_rate)\n self.num_channels += growth_rate * block_layers[0]\n self.num_channels = compression_rate * self.num_channels\n self.transition_1 = TransitionLayer(out_channels=int(self.num_channels))\n self.dense_block_2 = DenseBlock(num_layers=block_layers[1], growth_rate=growth_rate, drop_rate=drop_rate)\n self.num_channels += growth_rate * block_layers[1]\n self.num_channels = compression_rate * self.num_channels\n self.transition_2 = TransitionLayer(out_channels=int(self.num_channels))\n self.dense_block_3 = DenseBlock(num_layers=block_layers[2], growth_rate=growth_rate, drop_rate=drop_rate)\n self.num_channels += growth_rate * block_layers[2]\n self.num_channels = compression_rate * self.num_channels\n self.transition_3 = TransitionLayer(out_channels=int(self.num_channels))\n self.dense_block_4 = DenseBlock(num_layers=block_layers[3], growth_rate=growth_rate, drop_rate=drop_rate)\n\n self.P2 = GlobalAveragePooling2D()\n\n def call(self, inputs, training=None, **kwargs):\n x = self.C1(inputs)\n x = self.B1(x, training=training)\n x = self.A1(x)\n x = self.P1(x)\n\n x = self.dense_block_1(x, training=training)\n x = self.transition_1(x, training=training)\n x = self.dense_block_2(x, training=training)\n x = self.transition_2(x, training=training)\n x = self.dense_block_3(x, training=training)\n x = self.transition_3(x, training=training)\n x = self.dense_block_4(x, training=training)\n\n y = self.P2(x)\n\n return y","metadata":{"execution":{"iopub.status.busy":"2023-12-26T13:27:55.747224Z","iopub.execute_input":"2023-12-26T13:27:55.747793Z","iopub.status.idle":"2023-12-26T13:27:55.779469Z","shell.execute_reply.started":"2023-12-26T13:27:55.747751Z","shell.execute_reply":"2023-12-26T13:27:55.778502Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"def GELU(x):\n res = 0.5 * x * (1 + tf.nn.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * (x ** 3))))\n return res\n\n\nclass ResMLPBlock(tf.keras.layers.Layer):\n def __init__(self, units, residual_path):\n super(ResMLPBlock, self).__init__()\n self.residual_path = residual_path\n self.D1 = Dense(units, activation='relu')\n self.D2 = Dense(units, activation='relu')\n\n if self.residual_path:\n self.D3 = Dense(units)\n self.D4 = Dense(units)\n\n def call(self, inputs):\n residual = inputs\n\n x = self.D1(inputs)\n y = self.D2(x)\n\n if self.residual_path:\n residual = self.D3(inputs)\n residual = GELU(residual)\n residual = self.D4(residual)\n residual = GELU(residual)\n\n output = y + residual\n return output\n\n\n\nclass ResMLP(tf.keras.layers.Layer):\n def __init__(self, initial_filters, block_list, num_classes):\n super(ResMLP, self).__init__()\n self.initial_filters = initial_filters\n self.block_list = block_list\n\n self.D1 = Dense(self.initial_filters, activation='relu')\n self.B1 = BatchNormalization()\n\n self.blocks = tf.keras.models.Sequential()\n for block_id in range(len(block_list)):\n for layer_id in range(block_list[block_id]):\n if block_id != 0 and layer_id == 0:\n block = ResMLPBlock(units=self.initial_filters, residual_path=True)\n else:\n block = ResMLPBlock(units=self.initial_filters, residual_path=False)\n self.blocks.add(block)\n self.initial_filters *= 2\n\n self.D2 = Dense(num_classes, activation='softmax')\n\n\n def call(self, inputs):\n x = self.D1(inputs)\n x = self.B1(x)\n x = self.blocks(x)\n y = self.D2(x)\n return y","metadata":{"execution":{"iopub.status.busy":"2023-12-26T13:27:55.781862Z","iopub.execute_input":"2023-12-26T13:27:55.782527Z","iopub.status.idle":"2023-12-26T13:27:55.800046Z","shell.execute_reply.started":"2023-12-26T13:27:55.782488Z","shell.execute_reply":"2023-12-26T13:27:55.799037Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"class NeuralNetwork(tf.keras.Model):\n def __init__(self, initial_filters, block_list, num_classes):\n super(NeuralNetwork, self).__init__()\n self.layer1 = MyDenseNet(num_init_features=64, growth_rate=32, block_layers=[6, 12, 48, 32], compression_rate=0.5, drop_rate=0.5)\n self.layer2 = ResMLP(initial_filters=initial_filters, block_list=block_list, num_classes=num_classes)\n \n def call(self, x):\n x = self.layer1(x)\n y = self.layer2(x)\n return y","metadata":{"execution":{"iopub.status.busy":"2023-12-26T13:27:55.801315Z","iopub.execute_input":"2023-12-26T13:27:55.801641Z","iopub.status.idle":"2023-12-26T13:27:55.813978Z","shell.execute_reply.started":"2023-12-26T13:27:55.801613Z","shell.execute_reply":"2023-12-26T13:27:55.813065Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"net = NeuralNetwork(initial_filters=32, block_list=[2, 2], num_classes=2)\n\nnet.compile(optimizer=tf.keras.optimizers.Adamax(learning_rate=0.001),\n loss='categorical_crossentropy',\n metrics=['accuracy', 'AUC'])\n\nchechpoint_save_path = './MyNet.ckpt'\nif os.path.exists(chechpoint_save_path + '.index'):\n net.load_weights(chechpoint_save_path)\n\ncp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=chechpoint_save_path, save_best_only=True, save_weights_only=True) \n\nhistory = net.fit(train_ds, epochs=epochs, batch_size=batch_size, validation_data=valid_ds, callbacks=[cp_callback])\n\nnet.summary()\n\nfile = open('./Weights.txt', 'w')\nfor v in net.trainable_variables:\n file.write(str(v.name) + '\\n')\n file.write(str(v.shape) + '\\n')\n file.write(str(v.numpy()) + '\\n')\n \nfile.close()","metadata":{"execution":{"iopub.status.busy":"2023-12-26T13:27:55.817183Z","iopub.execute_input":"2023-12-26T13:27:55.817573Z","iopub.status.idle":"2023-12-26T13:33:35.012363Z","shell.execute_reply.started":"2023-12-26T13:27:55.817520Z","shell.execute_reply":"2023-12-26T13:33:35.011494Z"},"trusted":true},"execution_count":10,"outputs":[{"name":"stdout","text":"Epoch 1/30\n61/61 [==============================] - 50s 266ms/step - loss: 0.7149 - accuracy: 0.5364 - auc: 0.5448 - val_loss: 0.6984 - val_accuracy: 0.4917 - val_auc: 0.5034\nEpoch 2/30\n61/61 [==============================] - 10s 156ms/step - loss: 0.6728 - accuracy: 0.5946 - auc: 0.6300 - val_loss: 0.7136 - val_accuracy: 0.5000 - val_auc: 0.6045\nEpoch 3/30\n61/61 [==============================] - 10s 157ms/step - loss: 0.5814 - accuracy: 0.7089 - auc: 0.7644 - val_loss: 0.9685 - val_accuracy: 0.5000 - val_auc: 0.6091\nEpoch 4/30\n61/61 [==============================] - 9s 153ms/step - loss: 0.4573 - accuracy: 0.7952 - auc: 0.8653 - val_loss: 1.4441 - val_accuracy: 0.5000 - val_auc: 0.6600\nEpoch 5/30\n61/61 [==============================] - 10s 157ms/step - loss: 0.3851 - accuracy: 0.8347 - auc: 0.9065 - val_loss: 1.1465 - val_accuracy: 0.5333 - val_auc: 0.6839\nEpoch 6/30\n61/61 [==============================] - 9s 154ms/step - loss: 0.3203 - accuracy: 0.8742 - auc: 0.9360 - val_loss: 2.3332 - val_accuracy: 0.5000 - val_auc: 0.5500\nEpoch 7/30\n61/61 [==============================] - 9s 153ms/step - loss: 0.3039 - accuracy: 0.8753 - auc: 0.9421 - val_loss: 2.0688 - val_accuracy: 0.5000 - val_auc: 0.6128\nEpoch 8/30\n61/61 [==============================] - 10s 157ms/step - loss: 0.2304 - accuracy: 0.9064 - auc: 0.9682 - val_loss: 2.1779 - val_accuracy: 0.5000 - val_auc: 0.5624\nEpoch 9/30\n61/61 [==============================] - 9s 155ms/step - loss: 0.2308 - accuracy: 0.9148 - auc: 0.9658 - val_loss: 2.0346 - val_accuracy: 0.5000 - val_auc: 0.5846\nEpoch 10/30\n61/61 [==============================] - 9s 153ms/step - loss: 0.1868 - accuracy: 0.9293 - auc: 0.9784 - val_loss: 2.2855 - val_accuracy: 0.5000 - val_auc: 0.5260\nEpoch 11/30\n61/61 [==============================] - 9s 152ms/step - loss: 0.2321 - accuracy: 0.9127 - auc: 0.9664 - val_loss: 2.8612 - val_accuracy: 0.5000 - val_auc: 0.5375\nEpoch 12/30\n61/61 [==============================] - 9s 155ms/step - loss: 0.1831 - accuracy: 0.9220 - auc: 0.9798 - val_loss: 3.0464 - val_accuracy: 0.5000 - val_auc: 0.5417\nEpoch 13/30\n61/61 [==============================] - 9s 152ms/step - loss: 0.1785 - accuracy: 0.9418 - auc: 0.9788 - val_loss: 2.4974 - val_accuracy: 0.5250 - val_auc: 0.5847\nEpoch 14/30\n61/61 [==============================] - 9s 152ms/step - loss: 0.1838 - accuracy: 0.9293 - auc: 0.9791 - val_loss: 1.6423 - val_accuracy: 0.6167 - val_auc: 0.7265\nEpoch 15/30\n61/61 [==============================] - 9s 154ms/step - loss: 0.1736 - accuracy: 0.9387 - auc: 0.9805 - val_loss: 1.9749 - val_accuracy: 0.5667 - val_auc: 0.6785\nEpoch 16/30\n61/61 [==============================] - 9s 154ms/step - loss: 0.1592 - accuracy: 0.9439 - auc: 0.9837 - val_loss: 2.7532 - val_accuracy: 0.5167 - val_auc: 0.5588\nEpoch 17/30\n61/61 [==============================] - 9s 154ms/step - loss: 0.1279 - accuracy: 0.9574 - auc: 0.9883 - val_loss: 1.7602 - val_accuracy: 0.6167 - val_auc: 0.7065\nEpoch 18/30\n61/61 [==============================] - 10s 156ms/step - loss: 0.1498 - accuracy: 0.9459 - auc: 0.9856 - val_loss: 1.5741 - val_accuracy: 0.6583 - val_auc: 0.7322\nEpoch 19/30\n61/61 [==============================] - 9s 152ms/step - loss: 0.1636 - accuracy: 0.9418 - auc: 0.9820 - val_loss: 1.8949 - val_accuracy: 0.6250 - val_auc: 0.6865\nEpoch 20/30\n61/61 [==============================] - 10s 156ms/step - loss: 0.1392 - accuracy: 0.9491 - auc: 0.9874 - val_loss: 1.9107 - val_accuracy: 0.6250 - val_auc: 0.6859\nEpoch 21/30\n61/61 [==============================] - 9s 155ms/step - loss: 0.1344 - accuracy: 0.9543 - auc: 0.9884 - val_loss: 2.3945 - val_accuracy: 0.5583 - val_auc: 0.6155\nEpoch 22/30\n61/61 [==============================] - 9s 155ms/step - loss: 0.1362 - accuracy: 0.9584 - auc: 0.9873 - val_loss: 2.5436 - val_accuracy: 0.5250 - val_auc: 0.6148\nEpoch 23/30\n61/61 [==============================] - 9s 155ms/step - loss: 0.1388 - accuracy: 0.9543 - auc: 0.9875 - val_loss: 1.8095 - val_accuracy: 0.5833 - val_auc: 0.6978\nEpoch 24/30\n61/61 [==============================] - 9s 153ms/step - loss: 0.1379 - accuracy: 0.9522 - auc: 0.9876 - val_loss: 2.4057 - val_accuracy: 0.5250 - val_auc: 0.6265\nEpoch 25/30\n61/61 [==============================] - 10s 158ms/step - loss: 0.1088 - accuracy: 0.9584 - auc: 0.9931 - val_loss: 1.8513 - val_accuracy: 0.6167 - val_auc: 0.6970\nEpoch 26/30\n61/61 [==============================] - 9s 156ms/step - loss: 0.1687 - accuracy: 0.9324 - auc: 0.9830 - val_loss: 2.1456 - val_accuracy: 0.5417 - val_auc: 0.6541\nEpoch 27/30\n61/61 [==============================] - 9s 155ms/step - loss: 0.1680 - accuracy: 0.9335 - auc: 0.9826 - val_loss: 1.4942 - val_accuracy: 0.6083 - val_auc: 0.7281\nEpoch 28/30\n61/61 [==============================] - 10s 157ms/step - loss: 0.1327 - accuracy: 0.9522 - auc: 0.9889 - val_loss: 1.7868 - val_accuracy: 0.6167 - val_auc: 0.7002\nEpoch 29/30\n61/61 [==============================] - 9s 151ms/step - loss: 0.1113 - accuracy: 0.9615 - auc: 0.9914 - val_loss: 2.2745 - val_accuracy: 0.5667 - val_auc: 0.6497\nEpoch 30/30\n61/61 [==============================] - 9s 151ms/step - loss: 0.1206 - accuracy: 0.9574 - auc: 0.9884 - val_loss: 1.0820 - val_accuracy: 0.7583 - val_auc: 0.8236\nModel: \"neural_network\"\n_________________________________________________________________\nLayer (type) Output Shape Param # \n=================================================================\nmy_dense_net (MyDenseNet) multiple 18331328 \n_________________________________________________________________\nres_mlp (ResMLP) multiple 86818 \n=================================================================\nTotal params: 18,418,146\nTrainable params: 18,192,866\nNon-trainable params: 225,280\n_________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"code","source":"def plot_auc_acc_loss(history, epochs):\n tacc = history.history['accuracy']\n tloss = history.history['loss']\n tauc = history.history['auc']\n\n vacc = history.history['val_accuracy']\n vloss = history.history['val_loss']\n vauc = history.history['val_auc']\n\n Epochs = [i for i in range(epochs)]\n\n plt.style.use('fivethirtyeight')\n fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(24, 8))\n axes[0].plot(Epochs, tloss, 'r', label='Training loss')\n axes[0].plot(Epochs, vloss, 'g', label='Validation loss')\n axes[0].set_title('Training and Validation Loss')\n axes[0].set_xlabel('Epochs', fontsize=18)\n axes[0].set_ylabel('Loss', fontsize=18)\n axes[0].legend()\n\n axes[1].plot(Epochs, tacc, 'r', label='Training Accuracy')\n axes[1].plot(Epochs, vacc, 'g', label='Validation Accuracy')\n axes[1].set_title('Training and Validation Accuracy')\n axes[1].set_xlabel('Epochs', fontsize=18)\n axes[1].set_ylabel('Accuracy', fontsize=18)\n axes[1].legend()\n\n axes[2].plot(Epochs, tauc, 'r', label='Training AUC')\n axes[2].plot(Epochs, vauc, 'g', label='Validation AUC')\n axes[2].set_title('Training and Validation AUC')\n axes[2].set_xlabel('Epochs', fontsize=18)\n axes[2].set_ylabel('AUC', fontsize=18)\n axes[2].legend()\n\n plt.tight_layout()\n plt.show()\n\n return Epochs\n\n\nEpochs = plot_auc_acc_loss(history, epochs)","metadata":{"execution":{"iopub.status.busy":"2023-12-26T13:33:35.013587Z","iopub.execute_input":"2023-12-26T13:33:35.013879Z","iopub.status.idle":"2023-12-26T13:33:35.544402Z","shell.execute_reply.started":"2023-12-26T13:33:35.013852Z","shell.execute_reply":"2023-12-26T13:33:35.543411Z"},"trusted":true},"execution_count":11,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\ No newline at end of file diff --git a/Smile Recognition with Deep Learning/requirements.txt b/Smile Recognition with Deep Learning/requirements.txt new file mode 100644 index 000000000..fd88d2d25 --- /dev/null +++ b/Smile Recognition with Deep Learning/requirements.txt @@ -0,0 +1,6 @@ +numpy; +pandas; +matplotlib; +tensorflow; +keras; +sklearn \ No newline at end of file