Kaggle competition: SIIM-ISIC Melanoma Classification
<SOS> metric: MCAUC Kaggle data source: siim-isic-melanoma-classification<concatenate>
import numpy as np
import pandas as pd
train_images = np.concatenate(( train_imagesKeras,train_imagesKaggle), axis=0)
 print("new Concatenated train_images ", train_images.shape)
 print("_"*50)
 train_labels = np.concatenate(( train_labelsKeras,train_labelsKaggle), axis=0)
 print("new Concatenated train_labels ", train_labels.shape )<choose_model_class>
!wget -q -O other1.csv https://www.kaggleusercontent.com/kf/36961390/eyJhbGciOiJkaXIiLCJlbmMiOiJBMTI4Q0JDLUhTMjU2In0.. k0-Yl9R-H0lC86Nth0mIMQ.HiygAXAzwROTh0HZTHHGexh-f2l5eaPH1wFhHwsZxcrhqsddvY1Vrvy8xnKkzleMoYKR2DFzwKr3wpX3JkzC74BsxlPSpA5WeSAJkUitCktmGkcdB-EOiusI6rEBYMJUaXgx52hzF-dcVTPgRlj2HgnLCixBfs78h7mgFokLo1kZjgXjWlDHXcRao9DGKKHvp2EZxkGJ3-crDI_7kLxFfpDDlUjxmtLSZTk6yNxnExsj-nljUOan0tu7dHihhrrABxR7ZeT55MKqhkqew8slishHNIfnUvZug-n50E_yf0fjh8ydo7LUVYOVT4oY3WllNNhDc1PIEWXau9MPJITQZ6zW_KxYt9qRPkfCrXAr8UXtffECQ7Ut0zUcn7CUo1qen4fBLVhHnixAPnD6rFlv_23qvSgWRXbHQSVcjlSpC2tKWfVTFswN2zRREiahEiVbxWRH3vDrHk0uJeWSmHPUqS7cYT71h0bZhtmuOWd8T5hhk1hC4tECcSuZvxG80woxHsitF0YiQjDFQTTL7T3vUBSQYzLeAnTs4N5iZNVpyaIBVJJDCQ76yZ1lvDK9126jMASTxwnZVjb5mUAuYTpkxpS10tg-V8pzIsyoqitRAgWYiiMZ-AWdofBltCqZbW6ZCAshq9jSwr9dhjoDt1H5PD6m8A4Sko8i0oGH4YqoGMk.XzhuO7hsNjxzjnivzC2boA/sub_EfficientNetB3_384.csv
model = models.Sequential()
model.add(layers.Conv2D(32,(3, 3), activation='relu', input_shape=(28, 28, 1)))
 model.add(layers.Dropout(0.5))
model.add(layers.MaxPooling2D(( 2, 2)))
 model.add(layers.Conv2D(64,(3, 3), activation='relu'))
model.add(layers.Dropout(0.5))
model.add(layers.MaxPooling2D(( 2, 2)))
 model.add(layers.Conv2D(64,(3, 3), activation='relu'))
model.add(layers.Dropout(0.5))
model.add(layers.Flatten())
 model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))
model.compile(optimizer='rmsprop',loss='categorical_crossentropy',metrics=['accuracy'])
 print(model.summary() )<train_model>
a = pd.read_csv('other1.csv')
 b = pd.read_csv('.. /input/stack-models/submission.csv')
 c = pd.read_csv('.. /input/melanoma-efficientnet-b6-tpu-tta/submission.csv' )
<choose_model_class><EOS> 
pd.DataFrame({'image_name': a.image_name, 'target':(a.target + c.target*0.8)/2} ).to_csv('submission.csv', index=False )