import tensorflow as tf import matplotlib . pyplot as plt from tensorflow.keras import datasets, layers, models, losses ( x_train , y_train ), ( x_test , y_test )= tf .keras.datasets.mnist.load_data() x_train = tf .pad( x_train , [[ 0 , 0 ], [ 2 , 2 ], [ 2 , 2 ]])/ 255 x_test = tf .pad( x_test , [[ 0 , 0 ], [ 2 , 2 ], [ 2 , 2 ]])/ 255 x_train = tf .expand_dims( x_train , axis = 3 , name = None ) x_test = tf .expand_dims( x_test , axis = 3 , name = None ) x_val = x_train [- 2000 :,:,:,:] y_val = y_train [- 2000 :] x_train = x_train [:- 2000 ,:,:,:] y_train = y_train [:- 2000 ] model = models.Sequential([ layers.experimental.preprocessing.Resizing( 224 , 224 , interpolation = "bilinear" , input_shape = x_train .shape[ 1 :]), layers.Conv2D( 64 , 3 , strides = 1 , padding = 'same' ), layers.Activation( 'relu' ), layers.Conv2D( 64 , 3 , strides = 1 , padding = 'same' ), layers.Activation( 'relu' ), layers.MaxPooling2D( ...
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