Chars74K_Vgg16.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.5"},"colab":{"name":"Chars74K_Vgg16.ipynb","provenance":[],"collapsed_sections":[]},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"nybb7HGyjsiG","colab_type":"code","outputId":"7ce2f73f-1042-484e-f3f4-32cb6f3a40a4","executionInfo":{"status":"ok","timestamp":1576332842982,"user_tz":0,"elapsed":5978,"user":{"displayName":"Cheikh Gueye Wane","photoUrl":"","userId":"13986308290690852571"}},"colab":{"base_uri":"https://localhost:8080/","height":218}},"source":[" !wget --save-cookies cookies.txt --keep-session-cookies --no-check-certificate \"https://docs.google.com/uc?export=download&id=1AAWfvdHrH-GsrO5UFAVvGPBSWNB22R4l\" -O- | sed -rn \"s/.*confirm=([0-9A-Za-z_]+).*/Code: \\1\\n/p\""],"execution_count":0,"outputs":[{"output_type":"stream","text":["--2019-12-14 14:13:58--  https://docs.google.com/uc?export=download&id=1AAWfvdHrH-GsrO5UFAVvGPBSWNB22R4l\n","Resolving docs.google.com (docs.google.com)... 74.125.24.138, 74.125.24.100, 74.125.24.139, ...\n","Connecting to docs.google.com (docs.google.com)|74.125.24.138|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: unspecified [text/html]\n","Saving to: ‘STDOUT’\n","\n","\r-                       [<=>                 ]       0  --.-KB/s               \r-                       [ <=>                ]   3.17K  --.-KB/s    in 0s      \n","\n","2019-12-14 14:13:58 (16.7 MB/s) - written to stdout [3246]\n","\n","Code: 4yD2\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"9Juj5_nQyvTT","colab_type":"code","outputId":"f5ffbd3e-f04c-42c9-ca4a-a342e734109b","executionInfo":{"status":"ok","timestamp":1576332985968,"user_tz":0,"elapsed":14943,"user":{"displayName":"Cheikh Gueye Wane","photoUrl":"","userId":"13986308290690852571"}},"colab":{"base_uri":"https://localhost:8080/","height":322}},"source":["#in the below URl put the code from the output of the cell above 'Code :####'\n","\n","!wget --load-cookies cookies.txt \"https://docs.google.com/uc?export=download&confirm=4yD2&id=1AAWfvdHrH-GsrO5UFAVvGPBSWNB22R4l\" -O /tmp/dataset.zip\n","\n","import os\n","import zipfile\n","\n","local_zip = '/tmp/dataset.zip'\n","zip_ref = zipfile.ZipFile(local_zip, 'r')\n","zip_ref.extractall('/tmp')\n","zip_ref.close()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["--2019-12-14 14:16:12--  https://docs.google.com/uc?export=download&confirm=4yD2&id=1AAWfvdHrH-GsrO5UFAVvGPBSWNB22R4l\n","Resolving docs.google.com (docs.google.com)... 172.217.194.139, 172.217.194.100, 172.217.194.101, ...\n","Connecting to docs.google.com (docs.google.com)|172.217.194.139|:443... connected.\n","HTTP request sent, awaiting response... 302 Moved Temporarily\n","Location: https://doc-0g-94-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/5ioc6pkigisi1cnkmpgaivs7lohguptt/1576332000000/13986308290690852571/*/1AAWfvdHrH-GsrO5UFAVvGPBSWNB22R4l?e=download [following]\n","Warning: wildcards not supported in HTTP.\n","--2019-12-14 14:16:13--  https://doc-0g-94-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/5ioc6pkigisi1cnkmpgaivs7lohguptt/1576332000000/13986308290690852571/*/1AAWfvdHrH-GsrO5UFAVvGPBSWNB22R4l?e=download\n","Resolving doc-0g-94-docs.googleusercontent.com (doc-0g-94-docs.googleusercontent.com)... 172.217.26.65, 2404:6800:4003:80d::2001\n","Connecting to doc-0g-94-docs.googleusercontent.com (doc-0g-94-docs.googleusercontent.com)|172.217.26.65|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: unspecified [application/x-zip-compressed]\n","Saving to: ‘/tmp/dataset.zip’\n","\n","/tmp/dataset.zip        [        <=>         ] 172.74M   101MB/s    in 1.7s    \n","\n","2019-12-14 14:16:15 (101 MB/s) - ‘/tmp/dataset.zip’ saved [181133433]\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"scrolled":true,"id":"WxsrctWzZzUS","colab_type":"code","outputId":"675b9df2-34a7-48c7-c6ca-e3f28e0b47e7","executionInfo":{"status":"ok","timestamp":1576333068833,"user_tz":0,"elapsed":2259,"user":{"displayName":"Cheikh Gueye Wane","photoUrl":"","userId":"13986308290690852571"}},"colab":{"base_uri":"https://localhost:8080/","height":399}},"source":["from keras.preprocessing.image import ImageDataGenerator\n","from keras import layers\n","from keras import models\n","from keras import optimizers\n","from random import randint\n","import sys\n","import os\n","\n","import matplotlib.pyplot as plt\n","import matplotlib.image as mpimg\n","\n","base_dir = \"/tmp/dataset\"\n","\n","train = os.path.join(base_dir,\"train\")\n","\n","test  = os.path.join(base_dir,\"test\")\n","\n","# utils functions used to plot random image in dataset\n","\n","\n","def showFolderImageSample(base_folder, img_number=2):\n","    '''\n","    base_folder(String) : directory in which we'll look for sample\n","    img_number : for each folder how many img to show\n","    '''\n","    nrows = ncols = img_number\n","\n","    fig = plt.gcf()\n","    fig.set_size_inches(ncols * img_number, nrows * img_number)\n","    dir_names = os.listdir(base_folder)\n","\n","    # get sample directory\n","    sample_dir = []\n","    for i in range(img_number):\n","        sample_dir.append(dir_names[randint(0,len(dir_names)-1)])\n","    dir_names = [os.path.join(base_folder, dname) for dname in sample_dir]\n","\n","    print(\"total sample directory : {}\".format(len(dir_names)))\n","    # for each sample directory get img_number random image\n","\n","    img = []\n","    for d in dir_names:\n","        for i in range(img_number):\n","            img.append(os.path.join(d, os.listdir(d)[randint(0, len(os.listdir(d)) - 1)]))\n","\n","    for i, img_path in enumerate(img):\n","        # Set up subplot; subplot indices start at 1\n","        sp = plt.subplot(nrows, ncols, i + 1)\n","        sp.axis('Off')  # Don't show axes (or gridlines)\n","        img = mpimg.imread(img_path)\n","        plt.imshow(img)\n","\n","    plt.show()\n","    print(\"Size of one random image : {}\".format(img.shape))\n","    print(\" Example image : \\n {}\".format(img[0]))\n","\n","########################################################################\n","\n","\n","def dirTotalFile(base):\n","    fileNumber = 0\n","    if(os.path.isfile(base)):\n","        return 0\n","    _dir = os.listdir(base)\n","    for d in _dir:\n","        _d = os.path.join(base, d)\n","        if(os.path.isdir(_d)):\n","            fileNumber += dirTotalFile(_d)\n","        else:\n","            fileNumber += 1\n","    return fileNumber\n","\n","\n","###########################################boy li#############################\n","\n","showFolderImageSample(train,img_number=2)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["total sample directory : 2\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAOoAAADnCAYAAAAQCsqQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxlAbNJgANIbaoMEE4P8HccAQoJN5+OwAAAAASUVORK5CYII=\n","text/plain":["<Figure size 288x288 with 4 Axes>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["Size of one random image : (128, 128)\n"," Example image : \n"," [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n"," 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n"," 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n"," 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n"," 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n"," 1. 1. 1. 1. 1. 1. 1. 1.]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"SAiBUbKLZzUp","colab_type":"code","outputId":"efb90a74-433d-43e0-8d95-198d52e128f8","executionInfo":{"status":"ok","timestamp":1576333082479,"user_tz":0,"elapsed":2921,"user":{"displayName":"Cheikh Gueye Wane","photoUrl":"","userId":"13986308290690852571"}},"colab":{"base_uri":"https://localhost:8080/","height":67}},"source":["train_datagen = ImageDataGenerator(rescale=1./255,validation_split=0.2)\n","\n","val_datagen = ImageDataGenerator(rescale=1./255)\n","\n","test_datagen = ImageDataGenerator(rescale=1./255)\n","\n","train_generator = train_datagen.flow_from_directory(\n","    train,\n","    target_size=(128,128),\n","    batch_size=30,\n","    class_mode=\"categorical\",\n","    subset=\"training\"\n",")\n","\n","validation_generator = train_datagen.flow_from_directory(\n","    train,\n","    target_size=(128,128),\n","    batch_size=30,\n","    class_mode=\"categorical\",\n","    subset=\"validation\"\n",")\n","\n","test_generator = test_datagen.flow_from_directory(\n","    test,\n","    target_size=(128,128),\n","    batch_size=30,\n","    class_mode=\"categorical\"\n",")\n"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Found 49448 images belonging to 62 classes.\n","Found 12325 images belonging to 62 classes.\n","Found 12334 images belonging to 62 classes.\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"MFPhLsz-ZzUy","colab_type":"code","outputId":"7dbb47f3-04b1-41a4-ceca-38d36f368c94","executionInfo":{"status":"ok","timestamp":1576333714242,"user_tz":0,"elapsed":2415,"user":{"displayName":"Cheikh Gueye Wane","photoUrl":"","userId":"13986308290690852571"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["from keras import optimizers\n","from keras.applications import VGG16\n","\n","nodes = 32\n","\n","### LOADING VGG16 \n","\n","conv_base = VGG16(weights='imagenet',\n","            include_top=False,\n","            input_shape=(128,128,3))\n","\n","for layer in conv_base.layers:\n","    layer.trainable=False\n","    # if layer.name.split('_')[0]=='block5':\n","    #   layer.trainable=True\n","\n","conv_base.summary()\n","\n","# callbacks_list = [keras.callbacks.EarlyStopping(monitor='acc',patience=3,)\n","#                 ,keras.callbacks.ModelCheckpoint(filepath='checkpointmodel.h5',monitor='val_loss',save_best_only=True,)]\n","\n","\n","network = models.Sequential()\n","network.add(conv_base)\n","network.add(layers.Flatten())\n","network.add(layers.Dense(62, activation='softmax'))\n","\n","network.summary()\n"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Model: \"vgg16\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","input_4 (InputLayer)         (None, 128, 128, 3)       0         \n","_________________________________________________________________\n","block1_conv1 (Conv2D)        (None, 128, 128, 64)      1792      \n","_________________________________________________________________\n","block1_conv2 (Conv2D)        (None, 128, 128, 64)      36928     \n","_________________________________________________________________\n","block1_pool (MaxPooling2D)   (None, 64, 64, 64)        0         \n","_________________________________________________________________\n","block2_conv1 (Conv2D)        (None, 64, 64, 128)       73856     \n","_________________________________________________________________\n","block2_conv2 (Conv2D)        (None, 64, 64, 128)       147584    \n","_________________________________________________________________\n","block2_pool (MaxPooling2D)   (None, 32, 32, 128)       0         \n","_________________________________________________________________\n","block3_conv1 (Conv2D)        (None, 32, 32, 256)       295168    \n","_________________________________________________________________\n","block3_conv2 (Conv2D)        (None, 32, 32, 256)       590080    \n","_________________________________________________________________\n","block3_conv3 (Conv2D)        (None, 32, 32, 256)       590080    \n","_________________________________________________________________\n","block3_pool (MaxPooling2D)   (None, 16, 16, 256)       0         \n","_________________________________________________________________\n","block4_conv1 (Conv2D)        (None, 16, 16, 512)       1180160   \n","_________________________________________________________________\n","block4_conv2 (Conv2D)        (None, 16, 16, 512)       2359808   \n","_________________________________________________________________\n","block4_conv3 (Conv2D)        (None, 16, 16, 512)       2359808   \n","_________________________________________________________________\n","block4_pool (MaxPooling2D)   (None, 8, 8, 512)         0         \n","_________________________________________________________________\n","block5_conv1 (Conv2D)        (None, 8, 8, 512)         2359808   \n","_________________________________________________________________\n","block5_conv2 (Conv2D)        (None, 8, 8, 512)         2359808   \n","_________________________________________________________________\n","block5_conv3 (Conv2D)        (None, 8, 8, 512)         2359808   \n","_________________________________________________________________\n","block5_pool (MaxPooling2D)   (None, 4, 4, 512)         0         \n","=================================================================\n","Total params: 14,714,688\n","Trainable params: 0\n","Non-trainable params: 14,714,688\n","_________________________________________________________________\n","Model: \"sequential_3\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","vgg16 (Model)                (None, 4, 4, 512)         14714688  \n","_________________________________________________________________\n","flatten_3 (Flatten)          (None, 8192)              0         \n","_________________________________________________________________\n","dense_3 (Dense)              (None, 62)                507966    \n","=================================================================\n","Total params: 15,222,654\n","Trainable params: 507,966\n","Non-trainable params: 14,714,688\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"scrolled":true,"id":"AN-8VqgRZzVB","colab_type":"code","outputId":"363efda8-ec35-4f93-c925-5bd46978f751","executionInfo":{"status":"ok","timestamp":1576333990045,"user_tz":0,"elapsed":273741,"user":{"displayName":"Cheikh Gueye Wane","photoUrl":"","userId":"13986308290690852571"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["\n","epochs = 50\n","network.compile(loss='categorical_crossentropy',\n","                optimizer=optimizers.RMSprop(),\n","                metrics=['acc'])\n","\n","\n","history = network.fit_generator(\n","    train_generator,\n","    steps_per_epoch=100,\n","    epochs=epochs,\n","    verbose=1,\n","    validation_data=validation_generator,\n","    validation_steps=10\n",")\n","\n","\n","history_dict = history.history \n","loss_values = history_dict['loss']\n","val_loss_values = history_dict['val_loss']\n","acc_values = history_dict['acc']\n","val_acc_values = history_dict['val_acc']\n","plt.rcParams['figure.figsize'] = (16,8) # Make the figures a bit bigger\n","fig,(ax1,ax2,) = plt.subplots(1,2)\n","\n","x = range(1,epochs+1)\n","ax1.plot(x,loss_values,'bo',label='Training Loss')\n","ax1.plot(x,val_loss_values,'ro',label='Validation Loss')\n","ax1.set_title('Training and Validation loss ')\n","ax1.set_xlabel('Epochs')\n","ax1.set_ylabel('Loss')\n","ax1.legend()\n","\n","\n","ax2.plot(x,acc_values,'b',label='Training Accuracy')\n","ax2.plot(x,val_acc_values,'r',label='Validation Accuracy')\n","ax2.set_title('Training and Validation accuracy ')\n","ax2.set_xlabel('Epochs')\n","ax2.set_ylabel('Accuracy')\n","ax2.legend()\n","\n","plt.show()\n"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Epoch 1/50\n","100/100 [==============================] - 6s 60ms/step - loss: 2.1398 - acc: 0.4979 - val_loss: 0.9731 - val_acc: 0.7367\n","Epoch 2/50\n","100/100 [==============================] - 5s 55ms/step - loss: 1.1944 - acc: 0.6920 - val_loss: 0.6204 - val_acc: 0.8033\n","Epoch 3/50\n","100/100 [==============================] - 5s 54ms/step - loss: 1.0106 - acc: 0.7177 - val_loss: 0.8180 - val_acc: 0.7733\n","Epoch 4/50\n","100/100 [==============================] - 5s 54ms/step - loss: 0.9555 - acc: 0.7360 - val_loss: 0.6738 - val_acc: 0.7867\n","Epoch 5/50\n","100/100 [==============================] - 5s 55ms/step - loss: 0.9240 - acc: 0.7460 - val_loss: 0.6086 - val_acc: 0.8267\n","Epoch 6/50\n","100/100 [==============================] - 5s 54ms/step - loss: 0.8793 - acc: 0.7597 - val_loss: 0.8671 - val_acc: 0.7533\n","Epoch 7/50\n","100/100 [==============================] - 5s 54ms/step - loss: 0.8390 - acc: 0.7663 - val_loss: 0.4641 - val_acc: 0.8700\n","Epoch 8/50\n","100/100 [==============================] - 5s 54ms/step - loss: 0.8589 - acc: 0.7637 - val_loss: 0.4531 - val_acc: 0.8533\n","Epoch 9/50\n","100/100 [==============================] - 5s 54ms/step - loss: 0.7600 - acc: 0.7880 - val_loss: 0.6752 - val_acc: 0.8233\n","Epoch 10/50\n","100/100 [==============================] - 5s 54ms/step - loss: 0.8553 - acc: 0.7830 - val_loss: 0.6103 - val_acc: 0.8200\n","Epoch 11/50\n","100/100 [==============================] - 5s 54ms/step - loss: 0.7941 - acc: 0.7807 - val_loss: 0.5513 - val_acc: 0.8600\n","Epoch 12/50\n","100/100 [==============================] - 5s 54ms/step - loss: 0.7729 - acc: 0.7953 - val_loss: 0.6337 - val_acc: 0.8333\n","Epoch 13/50\n","100/100 [==============================] - 5s 53ms/step - loss: 0.6846 - acc: 0.8093 - val_loss: 0.6876 - val_acc: 0.8300\n","Epoch 14/50\n","100/100 [==============================] - 5s 54ms/step - loss: 0.7618 - acc: 0.7937 - val_loss: 0.5512 - val_acc: 0.8533\n","Epoch 15/50\n","100/100 [==============================] - 5s 54ms/step - loss: 0.7062 - acc: 0.8050 - val_loss: 0.6309 - val_acc: 0.8333\n","Epoch 16/50\n","100/100 [==============================] - 5s 54ms/step - loss: 0.6814 - acc: 0.8107 - val_loss: 0.5228 - val_acc: 0.8400\n","Epoch 17/50\n","100/100 [==============================] - 5s 54ms/step - loss: 0.6398 - acc: 0.8190 - val_loss: 0.6386 - val_acc: 0.8333\n","Epoch 18/50\n","100/100 [==============================] - 5s 54ms/step - loss: 0.6478 - acc: 0.8224 - val_loss: 0.5665 - val_acc: 0.8333\n","Epoch 19/50\n","100/100 [==============================] - 5s 53ms/step - loss: 0.6424 - acc: 0.8210 - val_loss: 0.5538 - val_acc: 0.8467\n","Epoch 20/50\n","100/100 [==============================] - 5s 54ms/step - loss: 0.5752 - acc: 0.8307 - val_loss: 0.5028 - val_acc: 0.8400\n","Epoch 21/50\n","100/100 [==============================] - 5s 53ms/step - loss: 0.5911 - acc: 0.8243 - val_loss: 0.5099 - val_acc: 0.8667\n","Epoch 22/50\n","100/100 [==============================] - 5s 54ms/step - loss: 0.5935 - acc: 0.8310 - val_loss: 0.6121 - val_acc: 0.8600\n","Epoch 23/50\n","100/100 [==============================] - 5s 54ms/step - loss: 0.6020 - acc: 0.8303 - val_loss: 0.5250 - val_acc: 0.8633\n","Epoch 24/50\n","100/100 [==============================] - 5s 54ms/step - loss: 0.6028 - acc: 0.8303 - val_loss: 0.7111 - val_acc: 0.8233\n","Epoch 25/50\n","100/100 [==============================] - 5s 55ms/step - loss: 0.5795 - acc: 0.8360 - val_loss: 0.6335 - val_acc: 0.8333\n","Epoch 26/50\n","100/100 [==============================] - 5s 55ms/step - loss: 0.5653 - acc: 0.8433 - val_loss: 0.5228 - val_acc: 0.8533\n","Epoch 27/50\n","100/100 [==============================] - 5s 53ms/step - loss: 0.6220 - acc: 0.8313 - val_loss: 0.5884 - val_acc: 0.8467\n","Epoch 28/50\n","100/100 [==============================] - 5s 54ms/step - loss: 0.5935 - acc: 0.8440 - val_loss: 0.5008 - val_acc: 0.8667\n","Epoch 29/50\n","100/100 [==============================] - 5s 54ms/step - loss: 0.5764 - acc: 0.8323 - val_loss: 0.7205 - val_acc: 0.8000\n","Epoch 30/50\n","100/100 [==============================] - 5s 54ms/step - loss: 0.5866 - acc: 0.8373 - val_loss: 0.5389 - val_acc: 0.8600\n","Epoch 31/50\n","100/100 [==============================] - 5s 54ms/step - loss: 0.5473 - acc: 0.8460 - val_loss: 0.5998 - val_acc: 0.8467\n","Epoch 32/50\n","100/100 [==============================] - 5s 54ms/step - loss: 0.6020 - acc: 0.8313 - val_loss: 0.3666 - val_acc: 0.8833\n","Epoch 33/50\n","100/100 [==============================] - 5s 54ms/step - loss: 0.6173 - acc: 0.8280 - val_loss: 0.4630 - val_acc: 0.8633\n","Epoch 34/50\n","100/100 [==============================] - 5s 54ms/step - loss: 0.5212 - acc: 0.8517 - val_loss: 0.6153 - val_acc: 0.8400\n","Epoch 35/50\n","100/100 [==============================] - 5s 54ms/step - loss: 0.4992 - acc: 0.8617 - val_loss: 0.5833 - val_acc: 0.8567\n","Epoch 36/50\n","100/100 [==============================] - 5s 54ms/step - loss: 0.5123 - acc: 0.8613 - val_loss: 0.5176 - val_acc: 0.8667\n","Epoch 37/50\n","100/100 [==============================] - 5s 54ms/step - loss: 0.5230 - acc: 0.8577 - val_loss: 0.5462 - val_acc: 0.8333\n","Epoch 38/50\n","100/100 [==============================] - 5s 54ms/step - loss: 0.5258 - acc: 0.8550 - val_loss: 0.4977 - val_acc: 0.8500\n","Epoch 39/50\n","100/100 [==============================] - 5s 54ms/step - loss: 0.5112 - acc: 0.8547 - val_loss: 0.5578 - val_acc: 0.8500\n","Epoch 40/50\n","100/100 [==============================] - 5s 54ms/step - loss: 0.4986 - acc: 0.8620 - val_loss: 0.6884 - val_acc: 0.8467\n","Epoch 41/50\n","100/100 [==============================] - 5s 54ms/step - loss: 0.5314 - acc: 0.8527 - val_loss: 0.5233 - val_acc: 0.8467\n","Epoch 42/50\n","100/100 [==============================] - 7s 66ms/step - loss: 0.5135 - acc: 0.8490 - val_loss: 0.4230 - val_acc: 0.8847\n","Epoch 43/50\n","100/100 [==============================] - 5s 54ms/step - loss: 0.4332 - acc: 0.8663 - val_loss: 0.6795 - val_acc: 0.8400\n","Epoch 44/50\n","100/100 [==============================] - 5s 54ms/step - loss: 0.5494 - acc: 0.8470 - val_loss: 0.3933 - val_acc: 0.8967\n","Epoch 45/50\n","100/100 [==============================] - 5s 54ms/step - loss: 0.5283 - acc: 0.8513 - val_loss: 0.6192 - val_acc: 0.8367\n","Epoch 46/50\n","100/100 [==============================] - 5s 54ms/step - loss: 0.4992 - acc: 0.8520 - val_loss: 0.6337 - val_acc: 0.8267\n","Epoch 47/50\n","100/100 [==============================] - 5s 54ms/step - loss: 0.5184 - acc: 0.8593 - val_loss: 0.4469 - val_acc: 0.8633\n","Epoch 48/50\n","100/100 [==============================] - 5s 54ms/step - loss: 0.5276 - acc: 0.8463 - val_loss: 0.4605 - val_acc: 0.8967\n","Epoch 49/50\n","100/100 [==============================] - 5s 54ms/step - loss: 0.5086 - acc: 0.8637 - val_loss: 0.4525 - val_acc: 0.8900\n","Epoch 50/50\n","100/100 [==============================] - 5s 54ms/step - loss: 0.4892 - acc: 0.8590 - val_loss: 0.5722 - val_acc: 0.8733\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA7YAAAHwCAYAAACSZPPAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nD+fuu+/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NGzaQkJBAQkICX3/9NVOnTqVMmTKEhITw1VdfAXD27FlOnTpF27ZtiY2NTS1k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1U5jlXlOFbV4XhVjmOVTfzcqsPxqhzHqnIcq+pwvCoX+7FqyTG2AAAAAIDm0aoVWwAAAABAk2i5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\n","text/plain":["<Figure size 1152x576 with 2 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"ICC_WOvcZzVL","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]}]}