<jupyter_start><jupyter_text>The resultResult slows that after training with data augmentation the DNN has a 100% validation accuracy and a 99.9% train accuracy, (which is without augmentation).<jupyter_code>model.load_weights('ASL-new-normal-weights.h5')
y_test = np.load('ASL_Train.npz')['arr_1']
y_test = y_test.astype('int8')
y_hat = model.predict_classes(trainX
                             )
pd.crosstab(y_test,y_hat)
test_wrong = [im for im in zip(trainX,y_hat,y_test) if im[1] != im[2]]
print("Wrong Test Cases:",len(test_wrong))


plt.figure(figsize=(10, 10))
for ind, val in enumerate(test_wrong[:100]):
    plt.subplots_adjust(left=0, right=1, bottom=0, top=1)
    plt.subplot(10, 10, ind + 1)
    im = 1 - val[0].reshape((100,100))
    plt.axis("off")
    plt.title(str('Pred: '+str(val[1]))+'\n'+str('True: '+str(val[2])), fontsize=14, color='black')
    plt.imshow(im, cmap='gray')<jupyter_output>4800/4800 [==============================] - 12s    
Wrong Test Cases: 9
StarcoderdataJupyterStructuredCleanDedup