AutoEncoder for Denoising Images.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"AutoEncoder for Denoising Images.ipynb","provenance":[],"collapsed_sections":[],"authorship_tag":"ABX9TyMBSXiSENPmHmmNGysAwK/d"},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"hu2Lbff3-Jrv","executionInfo":{"status":"ok","timestamp":1601720970788,"user_tz":-300,"elapsed":5430,"user":{"displayName":"Adeel Intizar","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiD26MVY4mIcjEVw1H2ZgOMRG7CDiB8egDU3-2g3Q=s64","userId":"11599686048572455421"}},"outputId":"c27f267c-018b-40f1-eaa3-5f253bc435df","colab":{"base_uri":"https://localhost:8080/","height":54}},"source":["from tensorflow.keras.layers import Conv2D, MaxPooling2D, UpSampling2D\n","from tensorflow.keras.callbacks import TensorBoard\n","from tensorflow.keras.datasets import mnist\n","import tensorflow as tf\n","import numpy as np\n","import matplotlib.pyplot as plt\n","\n","(x_train, _), (x_test, _) = mnist.load_data()\n","\n","x_train = x_train.astype('float32') / 255.\n","x_test = x_test.astype('float32') / 255.\n","x_train = np.reshape(x_train, (len(x_train), 28, 28, 1))\n","x_test = np.reshape(x_test, (len(x_test), 28, 28, 1))\n","\n","noise_factor = 0.5\n","x_train_noisy = x_train + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_train.shape) \n","x_test_noisy = x_test + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_test.shape) \n","\n","x_train_noisy = np.clip(x_train_noisy, 0., 1.)\n","x_test_noisy = np.clip(x_test_noisy, 0., 1.)\n"],"execution_count":2,"outputs":[{"output_type":"stream","text":["Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n","11493376/11490434 [==============================] - 0s 0us/step\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Ed9cHdce-nw5"},"source":["**Let's Plot the Noisy Images**"]},{"cell_type":"code","metadata":{"id":"G4J79bs9-mcL","executionInfo":{"status":"ok","timestamp":1601720975218,"user_tz":-300,"elapsed":2364,"user":{"displayName":"Adeel Intizar","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiD26MVY4mIcjEVw1H2ZgOMRG7CDiB8egDU3-2g3Q=s64","userId":"11599686048572455421"}},"outputId":"823e1431-5f2b-4411-f827-dfe4243d5dfa","colab":{"base_uri":"https://localhost:8080/","height":129}},"source":["n = 10\n","plt.figure(figsize=(20, 2))\n","for i in range(1, n + 1):\n","    ax = plt.subplot(1, n, i)\n","    plt.imshow(x_test_noisy[i].reshape(28, 28))\n","    plt.gray()\n","    ax.get_xaxis().set_visible(False)\n","    ax.get_yaxis().set_visible(False)\n","plt.show()"],"execution_count":3,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1440x144 with 10 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"wtVYIUF9-iSI","executionInfo":{"status":"ok","timestamp":1601720985582,"user_tz":-300,"elapsed":7049,"user":{"displayName":"Adeel Intizar","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiD26MVY4mIcjEVw1H2ZgOMRG7CDiB8egDU3-2g3Q=s64","userId":"11599686048572455421"}}},"source":["input_img = tf.keras.Input(shape=(28, 28, 1))\n","\n","x = Conv2D(32, (3, 3), activation='relu', padding='same')(input_img)\n","x = MaxPooling2D((2, 2), padding='same')(x)\n","x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)\n","encoded = MaxPooling2D((2, 2), padding='same')(x)\n","\n","# At this point the representation is (7, 7, 32)\n","\n","x = Conv2D(32, (3, 3), activation='relu', padding='same')(encoded)\n","x = UpSampling2D((2, 2))(x)\n","x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)\n","x = UpSampling2D((2, 2))(x)\n","decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)\n","\n","autoencoder = tf.keras.Model(input_img, decoded, name='AutoEncoder')\n","autoencoder.compile(optimizer='adam', loss='binary_crossentropy')"],"execution_count":4,"outputs":[]},{"cell_type":"code","metadata":{"id":"84m9q2UO_ScQ","executionInfo":{"status":"ok","timestamp":1601721290316,"user_tz":-300,"elapsed":303775,"user":{"displayName":"Adeel Intizar","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiD26MVY4mIcjEVw1H2ZgOMRG7CDiB8egDU3-2g3Q=s64","userId":"11599686048572455421"}},"outputId":"2e6d1774-31ac-4c63-ce96-42ac07cea756","colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["autoencoder.fit(x_train_noisy, x_train,\n","                epochs=100,\n","                batch_size=128,\n","                shuffle=True,\n","                validation_data=(x_test_noisy, x_test))"],"execution_count":5,"outputs":[{"output_type":"stream","text":["Epoch 1/100\n","469/469 [==============================] - 3s 7ms/step - loss: 0.1699 - val_loss: 0.1166\n","Epoch 2/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.1126 - val_loss: 0.1080\n","Epoch 3/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.1074 - val_loss: 0.1049\n","Epoch 4/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.1047 - val_loss: 0.1031\n","Epoch 5/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.1027 - val_loss: 0.1010\n","Epoch 6/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.1012 - val_loss: 0.0997\n","Epoch 7/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.1001 - val_loss: 0.0986\n","Epoch 8/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0992 - val_loss: 0.0979\n","Epoch 9/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0985 - val_loss: 0.0974\n","Epoch 10/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0981 - val_loss: 0.0973\n","Epoch 11/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0975 - val_loss: 0.0964\n","Epoch 12/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0971 - val_loss: 0.0962\n","Epoch 13/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0969 - val_loss: 0.0960\n","Epoch 14/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0966 - val_loss: 0.0961\n","Epoch 15/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0964 - val_loss: 0.0956\n","Epoch 16/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0961 - val_loss: 0.0955\n","Epoch 17/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0960 - val_loss: 0.0953\n","Epoch 18/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0958 - val_loss: 0.0952\n","Epoch 19/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0957 - val_loss: 0.0953\n","Epoch 20/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0955 - val_loss: 0.0953\n","Epoch 21/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0955 - val_loss: 0.0951\n","Epoch 22/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0953 - val_loss: 0.0948\n","Epoch 23/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0952 - val_loss: 0.0951\n","Epoch 24/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0951 - val_loss: 0.0945\n","Epoch 25/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0950 - val_loss: 0.0945\n","Epoch 26/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0949 - val_loss: 0.0947\n","Epoch 27/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0948 - val_loss: 0.0943\n","Epoch 28/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0948 - val_loss: 0.0944\n","Epoch 29/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0947 - val_loss: 0.0942\n","Epoch 30/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0947 - val_loss: 0.0943\n","Epoch 31/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0945 - val_loss: 0.0942\n","Epoch 32/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0945 - val_loss: 0.0943\n","Epoch 33/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0944 - val_loss: 0.0940\n","Epoch 34/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0944 - val_loss: 0.0940\n","Epoch 35/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0943 - val_loss: 0.0940\n","Epoch 36/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0943 - val_loss: 0.0941\n","Epoch 37/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0943 - val_loss: 0.0939\n","Epoch 38/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0942 - val_loss: 0.0939\n","Epoch 39/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0942 - val_loss: 0.0939\n","Epoch 40/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0941 - val_loss: 0.0939\n","Epoch 41/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0942 - val_loss: 0.0939\n","Epoch 42/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0941 - val_loss: 0.0939\n","Epoch 43/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0940 - val_loss: 0.0939\n","Epoch 44/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0940 - val_loss: 0.0938\n","Epoch 45/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0940 - val_loss: 0.0938\n","Epoch 46/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0939 - val_loss: 0.0936\n","Epoch 47/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0939 - val_loss: 0.0937\n","Epoch 48/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0939 - val_loss: 0.0936\n","Epoch 49/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0939 - val_loss: 0.0936\n","Epoch 50/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0938 - val_loss: 0.0937\n","Epoch 51/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0938 - val_loss: 0.0938\n","Epoch 52/100\n","469/469 [==============================] - 3s 7ms/step - loss: 0.0938 - val_loss: 0.0936\n","Epoch 53/100\n","469/469 [==============================] - 3s 7ms/step - loss: 0.0938 - val_loss: 0.0936\n","Epoch 54/100\n","469/469 [==============================] - 3s 7ms/step - loss: 0.0937 - val_loss: 0.0935\n","Epoch 55/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0937 - val_loss: 0.0936\n","Epoch 56/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0937 - val_loss: 0.0938\n","Epoch 57/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0937 - val_loss: 0.0937\n","Epoch 58/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0937 - val_loss: 0.0936\n","Epoch 59/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0937 - val_loss: 0.0937\n","Epoch 60/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0937 - val_loss: 0.0936\n","Epoch 61/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0936 - val_loss: 0.0935\n","Epoch 62/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0936 - val_loss: 0.0934\n","Epoch 63/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0936 - val_loss: 0.0935\n","Epoch 64/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0936 - val_loss: 0.0935\n","Epoch 65/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0935 - val_loss: 0.0934\n","Epoch 66/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0936 - val_loss: 0.0935\n","Epoch 67/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0936 - val_loss: 0.0936\n","Epoch 68/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0936 - val_loss: 0.0938\n","Epoch 69/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0935 - val_loss: 0.0932\n","Epoch 70/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0935 - val_loss: 0.0934\n","Epoch 71/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0935 - val_loss: 0.0936\n","Epoch 72/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0935 - val_loss: 0.0932\n","Epoch 73/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0935 - val_loss: 0.0935\n","Epoch 74/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0934 - val_loss: 0.0933\n","Epoch 75/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0934 - val_loss: 0.0933\n","Epoch 76/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0934 - val_loss: 0.0933\n","Epoch 77/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0934 - val_loss: 0.0937\n","Epoch 78/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0934 - val_loss: 0.0933\n","Epoch 79/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0934 - val_loss: 0.0936\n","Epoch 80/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0934 - val_loss: 0.0934\n","Epoch 81/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0934 - val_loss: 0.0933\n","Epoch 82/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0933 - val_loss: 0.0933\n","Epoch 83/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0934 - val_loss: 0.0934\n","Epoch 84/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0933 - val_loss: 0.0932\n","Epoch 85/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0933 - val_loss: 0.0932\n","Epoch 86/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0933 - val_loss: 0.0932\n","Epoch 87/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0933 - val_loss: 0.0932\n","Epoch 88/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0933 - val_loss: 0.0932\n","Epoch 89/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0933 - val_loss: 0.0932\n","Epoch 90/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0933 - val_loss: 0.0934\n","Epoch 91/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0932 - val_loss: 0.0932\n","Epoch 92/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0933 - val_loss: 0.0933\n","Epoch 93/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0933 - val_loss: 0.0933\n","Epoch 94/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0932 - val_loss: 0.0932\n","Epoch 95/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0932 - val_loss: 0.0932\n","Epoch 96/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0932 - val_loss: 0.0932\n","Epoch 97/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0932 - val_loss: 0.0932\n","Epoch 98/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0932 - val_loss: 0.0932\n","Epoch 99/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0932 - val_loss: 0.0932\n","Epoch 100/100\n","469/469 [==============================] - 3s 6ms/step - loss: 0.0932 - val_loss: 0.0932\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["<tensorflow.python.keras.callbacks.History at 0x7ff83806dfd0>"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"code","metadata":{"id":"ZWViU5KCAqqR","executionInfo":{"status":"ok","timestamp":1601721326880,"user_tz":-300,"elapsed":2847,"user":{"displayName":"Adeel Intizar","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiD26MVY4mIcjEVw1H2ZgOMRG7CDiB8egDU3-2g3Q=s64","userId":"11599686048572455421"}},"outputId":"d371fbd5-7cb8-46ee-c320-dc748a9831b2","colab":{"base_uri":"https://localhost:8080/","height":248}},"source":["decoded_imgs = autoencoder.predict(x_test_noisy)\n","\n","n = 10\n","plt.figure(figsize=(20, 4))\n","for i in range(1, n + 1):\n","    # Display original\n","    ax = plt.subplot(2, n, i)\n","    plt.imshow(x_test_noisy[i].reshape(28, 28))\n","    plt.gray()\n","    ax.get_xaxis().set_visible(False)\n","    ax.get_yaxis().set_visible(False)\n","\n","    # Display reconstruction\n","    ax = plt.subplot(2, n, i + n)\n","    plt.imshow(decoded_imgs[i].reshape(28, 28))\n","    plt.gray()\n","    ax.get_xaxis().set_visible(False)\n","    ax.get_yaxis().set_visible(False)\n","plt.show()\n"],"execution_count":7,"outputs":[{"output_type":"display_data","data":{"image/png":"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