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?
tensorflow cnn computer-vision mnist
asked 2 mins ago
MrRobot9MrRobot9
1154
$endgroup$
add a comment |
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?
tensorflow cnn computer-vision mnist
asked 2 mins ago
MrRobot9MrRobot9
1154
$endgroup$
add a comment |
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?
tensorflow cnn computer-vision mnist
asked 2 mins ago
MrRobot9MrRobot9
1154
$endgroup$
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
tensorflow cnn computer-vision mnist
asked 2 mins ago
MrRobot9MrRobot9
1154
asked 2 mins ago
MrRobot9MrRobot9
1154
asked 2 mins ago
MrRobot9MrRobot9
1154
asked 2 mins ago
MrRobot9MrRobot9
1154
asked 2 mins ago
MrRobot9MrRobot9
1154
1154
add a comment |
add a comment |
0
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