{"cells":[{"metadata":{},"cell_type":"markdown","source":"**Dogs VS Cats Classification Problem**"},{"metadata":{},"cell_type":"markdown","source":"The Dogs & Cats is a foundational problem for a basic CNN(convolutional neural network) model which involves classifying images as a dog or a cat.The dataset can be used for learning how to develop,evaluate and use convolutional deep learning neural networks for classification of images. This includes how to develop a robust test harness for estimating the performance of the model, exploring improvements for the model by changing the paramters of the model, saving and loading the model to make predicitions on new data. In this article, we will discover how to develop a CNN to classify images of dogs and cats.\n\nAfter reading this article, you will know :\n* How to load and prepare the images for training purpose.\n* How to split data for training and validation purpose.\n* How to apply Data Augmentation to the data.\n* How to develop a CNN model using keras and how to choose various parameters for improving performance of the model.\n* How to evaluate performance of our model.\n* How to save and load a model for further predictions.\n* How to draw the confusion matrix for trained model.\n* How to predict a new image from trained model."},{"metadata":{},"cell_type":"markdown","source":"The dataset can be downloaded for free from the Kaggle website.\nDownload the dataset by visiting the Dogs vs. Cats Data page and click the “Download All” button.\nDataset Link : https://www.kaggle.com/c/dogs-vs-cats\nThis will download the 850-megabyte file “dogs-vs-cats.zip” to your workstation.\nUnzip the file and you will see train.zip, train1.zip and a .csv file. Unzip the train.zip file, as we will be focusing only on this dataset.\nYou will now have a folder called ‘train/‘ that contains 25,000 .jpg files of dogs and cats. The photos are labeled by their filename, with the word “dog” or “cat“. "},{"metadata":{},"cell_type":"markdown","source":"**Import Library**"},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import os\nimport cv2\nimport random\nimport shutil\nimport numpy as np \nimport pandas as pd \nimport seaborn as sns\nfrom keras.models import load_model\nimport matplotlib.pyplot as plt\nfrom matplotlib.image import imread\nfrom sklearn.metrics import confusion_matrix,classification_report\nfrom keras.preprocessing.image import ImageDataGenerator,load_img\nfrom keras.models import Sequential,Model\nfrom keras.layers import Conv2D,Flatten,MaxPooling2D,Dense,GlobalAveragePooling2D,Dropout,BatchNormalization\nfrom sklearn.model_selection import train_test_split\nfrom keras.callbacks import ReduceLROnPlateau","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Prepare Training Data**"},{"metadata":{},"cell_type":"markdown","source":"Now our dataset contains only images of dogs and cats with filenames like 'dog.1.png' or 'cat.255.png'. Here we have prepared our label dataset by splitting image name with '.'  eg  if image name is 'dog.1.png' we split it by '.' as delimiter and extract first part of image name ('dog' here ) and if it is dog the we append 1 in list else we append 0 in list.So now basically we have our dataset comprising of images and labels corresponding to each images \n* if label = 1 , image corresponds to dog and \n* label = 0 if image corresponds to cat.\n\nWe have stored filenames and labels using pandas dataframe."},{"metadata":{"trusted":true},"cell_type":"code","source":"import zipfile\nwith zipfile.ZipFile(\"../input/dogs-vs-cats/\"+'train'+\".zip\",\"r\") as z:\n    z.extractall(\".\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"Y = []\npath = \"./train\"\nfilenames = os.listdir(path)\nfor img in os.listdir(path):\n    val = img.split(\".\")[0]\n    if val == \"dog\":\n        Y.append('1')\n    else:\n        Y.append('0')\n\ndf = pd.DataFrame({\n    'filename' : filenames,\n    'category' : Y\n})","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Here we see few initial values of our dataframe using df.head()"},{"metadata":{"trusted":true},"cell_type":"code","source":"df.head()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Here we see few last values of our dataframe using df.tail()"},{"metadata":{"trusted":true},"cell_type":"code","source":"df.tail()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Plot Random Dataset Image**"},{"metadata":{},"cell_type":"markdown","source":"Looking at a few random images in the directory, we can see that the images are coloured and have different shapes and sizes. For example, let’s load and plot some random images of dataset in a single figure. Running below code creates a figure showing some random images from the dataset.We can see that some images are landscape format, some are portrait format, and some are square."},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.figure(figsize=(12, 12))\nfor i in range(0, 9):\n    plt.subplot(4, 3, i+1)\n    sample = random.choice(filenames)\n    filename = path+'/'+sample\n    image = imread(filename)\n    plt.imshow(image)\nplt.tight_layout()\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Train Test Split**"},{"metadata":{},"cell_type":"markdown","source":"Here we are splitting our dataset into two parts. We are separating 20% of our dataset images for validation purpose and keeping rest for training purpose.This is done because we want to validate our trained model on a new set of images and not on the same set of images on which the model is trained. Therefore we want a different set of images apart from our training set upon which we can test our model and see the performance of the trained model on new unseen images."},{"metadata":{"trusted":true},"cell_type":"code","source":"train_df,val_df = train_test_split(df,test_size=0.2,random_state = 42)\ntrain_df = train_df.reset_index(drop=True)\nval_df = val_df.reset_index(drop=True)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Training Dataframe shape**"},{"metadata":{},"cell_type":"markdown","source":"To see our training dataframe shape we are using train_df.shape"},{"metadata":{"trusted":true},"cell_type":"code","source":"train_df.shape","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Validation Dataframe shape**"},{"metadata":{},"cell_type":"markdown","source":"To see our training dataframe shape we are using val_df.shape"},{"metadata":{"trusted":true},"cell_type":"code","source":"val_df.shape","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Total Count**"},{"metadata":{},"cell_type":"markdown","source":"Here by running below code we can see from the plot that images of both dogs and cats are almost equal in both our training and validation dataset. If there is too much difference in number of dogs and cats images then it can cause problem in training of our model"},{"metadata":{"trusted":true},"cell_type":"code","source":"train_df['category'].value_counts().plot.bar()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"val_df['category'].value_counts().plot.bar()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Selecting Image Size**"},{"metadata":{},"cell_type":"markdown","source":"The photos will have to be reshaped prior to modeling so that all images have the same shape. So we will choose some image height and width constants which will be used later to reshape all images while training process. Here we choose image height and image width as 128,128. That is 25,000 images with 128x128x3 pixels each, or 1,228,800,000 32-bit pixel values."},{"metadata":{},"cell_type":"markdown","source":"**Constants**"},{"metadata":{},"cell_type":"markdown","source":"Here we are defining constants to be used in our model code. So as to make our code modular we define all constants in a separate cell.\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"batch_size = 32\nepochs = 30\ntrain_size = train_df.shape[0]\nval_size = val_df.shape[0]\nimg_hieght = 128\nimg_width = 128\nimg_channels = 3","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Training Generator**"},{"metadata":{},"cell_type":"markdown","source":"Image data augmentation is a technique that can be used to artificially expand the size of a training dataset by creating modified versions of images in the dataset.Training deep learning neural network models on more data can result in more skillful models, and the augmentation techniques can create variations of the images that can improve the ability of the fit models to generalize what they have learned to new images. Data augmentation can also act as a regularization technique, adding noise to the training data, and encouraging the model to learn the same features, invariant to their position in the input. Small changes to the input photos of dogs and cats might be useful for this problem, such as small shifts and horizontal flips. These augmentations can be specified as arguments to the ImageDataGenerator used for the training dataset. The augmentations should not be used for the validation dataset, as we wish to evaluate the performance of the model on the unmodified photographs.                    \nWe can use the flow_from_dataframe() function on the data generator and create one iterator for each of the train and validation set. With the help of flow_from_dataframe method you can directly pass the Pandas DataFrame which has the mapping between filenames of the images and their labels..We must specify that the problem is a binary classification problem via the “class_mode” argument, and to load the images with the size of 128×128×3 pixels via the “target_size” argument. We will fix the batch size at 32."},{"metadata":{"trusted":true},"cell_type":"code","source":"train_datagen = ImageDataGenerator(\n    rescale = 1./255,\n    rotation_range = 15,\n    horizontal_flip = True,\n    zoom_range = 0.2,\n    shear_range = 0.1,\n    fill_mode = 'reflect',\n    width_shift_range = 0.1,\n    height_shift_range = 0.1\n)\n\ntrain_generator = train_datagen.flow_from_dataframe(\n    train_df,\n    \"./train\",\n    x_col = 'filename',\n    y_col = 'category',\n    target_size = (img_hieght,img_width),\n    batch_size = batch_size,\n    class_mode = 'binary'\n)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Working of Image Generator**"},{"metadata":{},"cell_type":"markdown","source":"Here we will see working of our training image generator by applying augmentation on a single image selected from our training dataframe."},{"metadata":{"trusted":true},"cell_type":"code","source":"example_df = train_df.sample(n=1)\nexample_generator = train_datagen.flow_from_dataframe(\n    example_df, \n    \"./train\",\n    x_col='filename',\n    y_col='category',\n    target_size=(img_hieght,img_width),\n    class_mode='raw'\n)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"We can see by running below code 9 different variations of a single image created by data augmentation techniques. We can see how these 9 images differ only by some properties like horizontal flip, zoom etc from each other."},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.figure(figsize=(12, 12))\nfor i in range(0, 9):\n    plt.subplot(4, 3, i+1)\n    for X_batch, Y_batch in example_generator:\n        image = X_batch[0]\n        plt.imshow(image)\n        break\nplt.tight_layout()\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Validation Generator**"},{"metadata":{},"cell_type":"markdown","source":"We will not apply any data augmentation technique like horizontal flip, zoom etc on validation set because we are not going to use this for training purpose."},{"metadata":{"trusted":true},"cell_type":"code","source":"val_datagen = ImageDataGenerator(\n    rescale = 1./255,\n)\n\nval_generator = val_datagen.flow_from_dataframe(\n    val_df,\n    \"./train\",\n    x_col = 'filename',\n    y_col = 'category',\n    target_size = (img_hieght,img_width),\n    batch_size = batch_size,\n    class_mode = 'binary'\n)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Building the Model**"},{"metadata":{},"cell_type":"markdown","source":"In this section, we can develop a convolutional neural network model for the dogs vs. cats dataset.\n\n*Layers needed by CNN*\n\n* ** Conv2D** :- Basic Convolutional layer which is used to extract features from our image.\n* ** MaxPooling ** :- CNN has a concept of max pooling. After every convoulution we get some values in a kernel. However in max pooling we select max kernel value.It helps us to reduce unnecessary noise from our data and keep only useful values for training.\n* **  BatchNormalization** :- This layer helps for normalization of our input values which helps in fast learning of our model.\n* **  Droupout** :- Dropout works by probabilistically removing, or “dropping out,” inputs to a layer, which may be input variables in the data sample or activations from a previous layer. It has the effect of simulating a large number of networks with very different network structures and, in turn, making nodes in the network generally more robust to the inputs.\n* ** Dense** :- Dense layer is needed by every neural network to finally output the result however every once in while using a Dense layer helps in making model learn.\n* **  Flatten**:- Conv2D layer returns doesn't return a flatten data hence we need Flatten layer before feeding it into final Dense layer.\n\nIn the following architecture filter size goes on increasing in each consecutive cnn layer from 32,64,128 to 256. The reason for this is that as we move forward in the layers the patterns gets more complex, hence we have to capture larger combinations of patterns. That's why we increase filter size in the subsequent layers to capture as many combinations as possible.Also here I tested model with both 3 CNN layers and 4 CNN layers and got good accuracy with 4 CNN layers so due to that I took 4 CNN layers in the final model."},{"metadata":{},"cell_type":"markdown","source":"![image.png](attachment:image.png)\nThis image is just an example of cnn model, it not describes our exact model","attachments":{"image.png":{"image/png":"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"}}},{"metadata":{"trusted":true},"cell_type":"code","source":"model = Sequential()\n\nmodel.add(Conv2D(32,(3,3),activation='relu',input_shape = (img_hieght,img_width,img_channels)))\nmodel.add(BatchNormalization())\nmodel.add(MaxPooling2D(pool_size=(2,2)))\nmodel.add(Dropout(0.25))\n\nmodel.add(Conv2D(64,(3,3),activation='relu'))\nmodel.add(BatchNormalization())\nmodel.add(MaxPooling2D(pool_size=(2,2)))\nmodel.add(Dropout(0.25))\n\nmodel.add(Conv2D(128,(3,3),activation='relu'))\nmodel.add(BatchNormalization())\nmodel.add(MaxPooling2D(pool_size=(2,2)))\nmodel.add(Dropout(0.25))\n\nmodel.add(Conv2D(256,(3,3),activation='relu'))\nmodel.add(BatchNormalization())\nmodel.add(MaxPooling2D(pool_size=(2,2)))\nmodel.add(Dropout(0.25))\n\nmodel.add(Flatten())\nmodel.add(Dense(512,activation='relu'))\nmodel.add(BatchNormalization())\nmodel.add(Dropout(0.25))\nmodel.add(Dense(1,activation='sigmoid'))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Callbacks**"},{"metadata":{},"cell_type":"markdown","source":"Models often benefit from reducing the learning rate by a factor once learning stagnates. This callback monitors a quantity and if no improvement is seen for a 'patience' number of epochs, the learning rate is reduced.Basically if learning rate is too low the model requires many updates before reaching minimum point so we need to increase leraning rate but if it gets increaed too much then it causes drastic updates which leads to degradation of performance so at that time we need to reduce the learning rate. We are taking care of this thing in our model with this callback (ReduceLROnPlateau)."},{"metadata":{},"cell_type":"markdown","source":"![image.png](attachment:image.png)","attachments":{"image.png":{"image/png":"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"}}},{"metadata":{"trusted":true},"cell_type":"code","source":"learning_rate_reduction = ReduceLROnPlateau(monitor = 'val_accuracy',\n                                            patience=2,\n                                            factor=0.5,\n                                            min_lr = 0.00001,\n                                            verbose = 1)\ncallbacks = [learning_rate_reduction]","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"model.summary() gives the description of the architecture of our model."},{"metadata":{"trusted":true},"cell_type":"code","source":"model.summary()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Finally we will compile the model .There are 3 things to mention here : Optimizer,Loss, Metrics\n* Optimizer :- To minimize cost function we use different methods For ex :- like gradient descent, stochastic gradient descent. So these are call optimizers. We are using a default one here which is adam.\n* Loss :- To make our model better we either minimize loss or maximize accuracy. Neural Networks always minimize loss. To measure it we can use different formulas like 'categorical_crossentropy' or 'binary_crossentropy'. Here I have used binary_crossentropy.\n* Metrics :- This is to denote the measure of your model. Can be accuracy or some other metric."},{"metadata":{"trusted":true},"cell_type":"code","source":"model.compile(optimizer='Adam',loss='binary_crossentropy',metrics=['accuracy'])","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Fit Model**"},{"metadata":{},"cell_type":"markdown","source":"We are now going to train our compiled model using the train iterator (train_generator) and use the val iterator (val_generator) as a validation dataset during training.The number of steps for the train and validation iterators must be specified. This is the number of batches that will comprise one epoch. This can be specified via the length of each iterator, and will be the total number of images in the train and validation directories divided by the batch size (32).The model will be fit for 30 epochs."},{"metadata":{"trusted":true},"cell_type":"code","source":"model.fit_generator(\n    train_generator,\n    epochs = epochs,\n    validation_data =  val_generator,\n    steps_per_epoch = train_size//batch_size,\n    validation_steps = val_size//batch_size,\n    callbacks = callbacks\n)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Saving the Model**"},{"metadata":{},"cell_type":"markdown","source":"Once fit, we can save the final model to an .h5 file by calling the save() function on the model and pass in the chosen filename."},{"metadata":{"trusted":true},"cell_type":"code","source":"model.save(\"model.h5\")","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Below code can be used to load model weights from save model.h5 file. So that we don't have to go through full trainnig process again."},{"metadata":{"trusted":true},"cell_type":"code","source":"# model = load_model(\"/kaggle/input/mode-file/model.h5\")","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Validation Accuracy**"},{"metadata":{},"cell_type":"markdown","source":"Now we will calculate our model's accuracy on validation set with use of keras  *.evaluate_generator()* function. We have used score[1]*100 because output of evaluate_generator() is a list of which second element represents accuracy of model on passed dataset."},{"metadata":{"trusted":true},"cell_type":"code","source":"score = model.evaluate_generator(val_generator)\nprint(score[1]*100)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Training Accuracy**"},{"metadata":{},"cell_type":"markdown","source":"Here we are going to calculate model's accuracy on training set itself."},{"metadata":{"trusted":true},"cell_type":"code","source":"score = model.evaluate_generator(train_generator)\nprint(score[1]*100)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Confusion Matrix**"},{"metadata":{},"cell_type":"markdown","source":"A confusion matrix is a table that is often used to describe the performance of a classification model (or \"classifier\") on a set of test data for which the true values are known.So here we are going to draw confusion matrix for our model with use of sklearn.metrics.confusion_matrix inbuilt function. What can we learn from this matrix ?\n* There are two possible predicted classes: 0 and 1. If we were predicting the dog, for example, 1 would mean image represents dog, and 0 would mean that image represents cat.\n* true positives (TP): These are cases in which we predicted 1 (the image is of dog), and the image is of dog.\n* true negatives (TN): We predicted 0, and the image is of cat.\n* false positives (FP): We predicted 1, but the image is of cat. (Also known as a \"Type I error.\")\n* false negatives (FN): We predicted 0, but the image is of dog. (Also known as a \"Type II error.\")"},{"metadata":{},"cell_type":"markdown","source":"We want to draw confusion matrix on validation dataset for our trained model so for that purpose we need to use argument shuffle = \"False\" in our generator because we want predicted and true labels for validation dataset. So for that we made two iterators to validation dataset in which one is used as validation dataset (val_generator) and one (conf_generator) for drawing confusion matrix."},{"metadata":{"trusted":true},"cell_type":"code","source":"conf_datagen = ImageDataGenerator(\n    rescale = 1./255,\n)\n\nconf_generator = conf_datagen.flow_from_dataframe(\n    val_df,\n    \"./train\",\n    x_col = 'filename',\n    y_col = 'category',\n    target_size = (img_hieght,img_width),\n    batch_size = batch_size,\n    shuffle = False,\n    class_mode = 'binary'\n)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_predict = model.predict_generator(conf_generator)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_predict = np.where(y_predict > 0.5, 1, 0)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"p = conf_generator.classes\nq = y_predict\np = np.array(p)\nq = q.flatten()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"cfm = confusion_matrix(p, q)\nprint(cfm)\nax= plt.subplot()\nsns.heatmap(cfm, annot=True, ax = ax);\n# labels, title and ticks\nax.set_xlabel('Predicted labels')\nax.set_ylabel('True labels');\nax.set_title('Confusion Matrix');\nax.xaxis.set_ticklabels(['cats', 'dogs'])\nax.yaxis.set_ticklabels(['cats', 'dogs'])","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"The classification report visualizer displays the precision, recall, F1, and support scores for the model.Here we are using sklearn.metrics.classification_reports function to see the measures."},{"metadata":{"trusted":true},"cell_type":"code","source":"print(classification_report(p,q))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Prediction**"},{"metadata":{},"cell_type":"markdown","source":"Here we will see the prediction of our model on a new image with use of model.predict function"},{"metadata":{"trusted":true},"cell_type":"code","source":"path = \"/kaggle/input/test-images/dog.jpeg\"\nimg = cv2.imread(path)\nplt.imshow(img)\nimg = cv2.resize(img,(128,128))\nimg = np.reshape(img,[1,128,128,3])\nimg = np.divide(img,255)\nresult = model.predict(img)\nif result[0] >= 0.5:\n    print(\"According to our model's prediction below image is of a Dog\")\nelse:\n    print(\"According to our model's prediction below image is of a Cat\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"shutil.rmtree(\"./train\")","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Resnet**"},{"metadata":{},"cell_type":"markdown","source":"Transfer learning involves using all or parts of a model trained on a related task.Keras provides a range of pre-trained models that can be loaded and used wholly or partially via the Keras Applications API.\nA useful model for transfer learning is one of the ResNet models, such as ResNet-50 with 50 layers that at the time it was developed, achieved top results. We can use the feature extraction part of the model and add a new classifier part of the model that is tailored to the dogs and cats dataset. Specifically, we can hold the weights of all of the convolutional layers fixed during training, and only train new fully connected layers that will learn to interpret the features extracted from the model and make a binary classification.\nThis can be achieved by loading the ResNet-50 model, removing the fully connected layers from the output-end of the model, then adding the new fully connected layers to interpret the model output and make a prediction. The classifier part of the model can be removed automatically by setting the “include_top” argument to “False“, which also requires that the shape of the input also be specified for the model, in this case (128, 128, 3)."},{"metadata":{"trusted":true},"cell_type":"code","source":"# from keras.applications.resnet50 import ResNet50","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# base_model = ResNet50(include_top=False,weights=None,input_shape=(128,128,3))\n# res_model = base_model.output\n# res_model = GlobalAveragePooling2D()(res_model)\n# res_model = Dropout(0.5)(res_model)\n# predictions = Dense(1,activation='sigmoid')(res_model)\n# model = Model(inputs = base_model.input,outputs = predictions)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Author : Kanish Anand , AI data engineer"}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":1}