Package: dl-assistant
PyPi Library :- dl-assistant DocumentationTable of ContentsInstallationUsage - Image ClassificationExamplesAPI ReferenceInstallationIt can be easily installed by typing the following in the terminal:pipinstalldl-assistantUsage - Image classificationcreate_dataframe- This function takes dir containing training or test data and convert it into pandas dataframe that is 1st step towards deep learningSimple Usagefrom dl_assistant.image_cnn
                        import classification
                        x=classification()
                        df=x.create_dataframe('train') # It will create a dataframe getting a datframe with image address in one column and label in another
                        #Continue on your ownprep_x_train- This function takes image paths, height and width to resize and returns an array of features of training data. It itself divide features by 255.0Simple Usagefrom dl_assistant.image_cnn import classification
                        x=classification()
                        df=x.create_dataframe('train') # It will create a dataframe getting a datframe with image address in one column and label in another
                        x_train = x.prep_x_train(df['Images',100,100])# get features divided by 255.0prep_y_train- This function takes labels and number of classes as input and converts labels into binary metrics.Simple Usagefrom dl_assistant.image_cnn import classification
                        x=classification()
                        df=x.create_dataframe('train') # It will create a dataframe getting a datframe with image address in one column and label in another
                        x_train = x.prep_x_train(df['Images',100,100])
                        y_train = x.prep_y_train(df['Labels'],7)make_model- This function takes unit of 1 stconv2D layer and number of classes as input and returns a basic non-trained model.Simple Usagefrom dl_assistant.image_cnn import classification
                        x=classification()
                        df=x.create_dataframe('train') # It will create a dataframe getting a datframe with image address in one column and label in another
                        x_train = x.prep_x_train(df['Images',100,100])
                        y_train = x.prep_y_train(df['Labels'],7)
                        shape=x_train[0].shape
                        model = x.make_model(128,7,shape)expert_make_model- This is a function developed for expert.Syntax=expert_make_model(self,layers,unit,num_classes,input_shape,dr)layers= This is a string list that can contain either Conv2D,MaxPooling2D or Dropoutunit= This is the integer that contains the unit of 1st Conv2D layernum_classes= This is the count of no. of labelsinput_shape= This is the input shape or shape of x_train[0]dr= The dropout rateSimple Usagefrom dl_assistant.image_cnn import classification
                        x=classification()
                        df=x.create_dataframe('train') # It will create a dataframe getting a datframe with image address in one column and label in another
                        x_train = x.prep_x_train(df['Images',100,100])
                        y_train = x.prep_y_train(df['Labels'],7)
                        shape=x_train[0].shape
                        model = x.expert_make_model(['Conv2D','Conv2D','MaxPooling2D','Dropout'],128,7,shape,0.5)train_model- This function takes model to train, x_train , y_train, epochs and batch_size and returns a trained modelSimple Usagefrom dl_assistant.image_cnn import classification
                        x=classification()
                        df=x.create_dataframe('train') # It will create a dataframe getting a datframe with image address in one column and label in another
                        x_train = x.prep_x_train(df['Images',100,100])
                        y_train = x.prep_y_train(df['Labels'],7)
                        shape=x_train[0].shape
                        model = x.make_model(128,7,shape)
                        batch_size=32
                        epochs=70
                        model.x.train_model(model,x_train,y_train,batch_size,epochs)ExamplesCreate a emotion-classification model`from dl_assistant.image_cnn import classification                      model = classification()                     model = model.create('TRAIN',7)                     model.predict([x_test[0]])`