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)
])

optimizer = tf.keras.optimizers.RMSprop(lr=0.00020, rho=0.99, epsilon=1e-8, decay=0.0)

model.compile(optimizer=optimizer,loss='sparse_categorical_crossentropy',metrics=['accuracy'])

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?

$\endgroup$
3
  • $\begingroup$ by the first line, you mean sequential or max-pool layer? if max-pool why you put max-pool layer as the first layer of your model? $\endgroup$
    – Hunar
    Apr 24, 2019 at 8:06
  • $\begingroup$ @SoK its a challenge, it becomes more difficult to improve accuracy that way $\endgroup$
    – MrRobot9
    Apr 24, 2019 at 8:08
  • $\begingroup$ Ok, try to put dropout before each dense layer, and play with divers dropout rates. $\endgroup$
    – Hunar
    Apr 24, 2019 at 8:11

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