Extending what Convolutional Neural Nets can do

Source: Cognitiveclass.ai

Hands on with CNN

Output with DNN
Output with the Convolutions and max poolings
The visualization

How convolutions work, hands-on ?(OPTIONAL)

Vertical line filter
With pooling

Excercise 3

My Solution

My solution
class myCallback(tf.keras.callbacks.Callback):def on_epoch_end(self, epoch, logs={}):if(logs.get('acc')>0.998):print("/n Reached 99.8% accuracy so cancelling training!")self.model.stop_training = True
training_images=training_images.reshape(60000, 28, 28, 1)
test_images=test_images.reshape(10000, 28, 28, 1)
training_images = training_images / 255.0
test_images = test_images / 255.0
# YOUR CODE ENDS HERE
model = tf.keras.models.Sequential([
# YOUR CODE STARTS HERE
tf.keras.layers.Conv2D(64, (3,3), activation='relu', input_shape=(28, 28, 1)),
tf.keras.layers.MaxPooling2D(2, 2),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(256, activation='relu'),
tf.keras.layers.Dense(10, activation='softmax')
# YOUR CODE ENDS HERE
])
My output

About Me

High School,Ted-X,Ted-Ed Speaker|Mentor,@tfugmumbai|@Microsoft Student Ambassador|International Speaker

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