Better way to deal with downsampled MNIST images












0












$begingroup$


model = tf.keras.models.Sequential([
tf.keras.layers.MaxPool2D(4, 4, input_shape=(28,28,1)),
tf.keras.layers.Conv2D(32, (5, 5), padding='same', activation=tf.nn.relu),
tf.keras.layers.MaxPool2D(2, 2),
tf.keras.layers.Dropout(0.25),

tf.keras.layers.Conv2D(128, (3, 3), padding='same', activation=tf.nn.relu),
tf.keras.layers.Conv2D(128, (3, 3), padding='same', activation=tf.nn.relu),
tf.keras.layers.MaxPool2D(2, 2),
tf.keras.layers.Dropout(0.25),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(512, activation=tf.nn.relu),
tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])


So, the MNIST images are downsampled from 28*28 to 7*7 from the first line. Using that,I want to get a good accuracy and the maximum I'm getting is 89% with 40 epoch and 6000 test images. How can I improve this without removing the first line?









share









$endgroup$

















    0












    $begingroup$


    model = tf.keras.models.Sequential([
    tf.keras.layers.MaxPool2D(4, 4, input_shape=(28,28,1)),
    tf.keras.layers.Conv2D(32, (5, 5), padding='same', activation=tf.nn.relu),
    tf.keras.layers.MaxPool2D(2, 2),
    tf.keras.layers.Dropout(0.25),

    tf.keras.layers.Conv2D(128, (3, 3), padding='same', activation=tf.nn.relu),
    tf.keras.layers.Conv2D(128, (3, 3), padding='same', activation=tf.nn.relu),
    tf.keras.layers.MaxPool2D(2, 2),
    tf.keras.layers.Dropout(0.25),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(512, activation=tf.nn.relu),
    tf.keras.layers.Dense(10, activation=tf.nn.softmax)
    ])


    So, the MNIST images are downsampled from 28*28 to 7*7 from the first line. Using that,I want to get a good accuracy and the maximum I'm getting is 89% with 40 epoch and 6000 test images. How can I improve this without removing the first line?









    share









    $endgroup$















      0












      0








      0





      $begingroup$


      model = tf.keras.models.Sequential([
      tf.keras.layers.MaxPool2D(4, 4, input_shape=(28,28,1)),
      tf.keras.layers.Conv2D(32, (5, 5), padding='same', activation=tf.nn.relu),
      tf.keras.layers.MaxPool2D(2, 2),
      tf.keras.layers.Dropout(0.25),

      tf.keras.layers.Conv2D(128, (3, 3), padding='same', activation=tf.nn.relu),
      tf.keras.layers.Conv2D(128, (3, 3), padding='same', activation=tf.nn.relu),
      tf.keras.layers.MaxPool2D(2, 2),
      tf.keras.layers.Dropout(0.25),
      tf.keras.layers.Flatten(),
      tf.keras.layers.Dense(512, activation=tf.nn.relu),
      tf.keras.layers.Dense(10, activation=tf.nn.softmax)
      ])


      So, the MNIST images are downsampled from 28*28 to 7*7 from the first line. Using that,I want to get a good accuracy and the maximum I'm getting is 89% with 40 epoch and 6000 test images. How can I improve this without removing the first line?









      share









      $endgroup$




      model = tf.keras.models.Sequential([
      tf.keras.layers.MaxPool2D(4, 4, input_shape=(28,28,1)),
      tf.keras.layers.Conv2D(32, (5, 5), padding='same', activation=tf.nn.relu),
      tf.keras.layers.MaxPool2D(2, 2),
      tf.keras.layers.Dropout(0.25),

      tf.keras.layers.Conv2D(128, (3, 3), padding='same', activation=tf.nn.relu),
      tf.keras.layers.Conv2D(128, (3, 3), padding='same', activation=tf.nn.relu),
      tf.keras.layers.MaxPool2D(2, 2),
      tf.keras.layers.Dropout(0.25),
      tf.keras.layers.Flatten(),
      tf.keras.layers.Dense(512, activation=tf.nn.relu),
      tf.keras.layers.Dense(10, activation=tf.nn.softmax)
      ])


      So, the MNIST images are downsampled from 28*28 to 7*7 from the first line. Using that,I want to get a good accuracy and the maximum I'm getting is 89% with 40 epoch and 6000 test images. How can I improve this without removing the first line?







      tensorflow cnn computer-vision mnist





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      asked 2 mins ago









      MrRobot9MrRobot9

      1154




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