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Give 99.28% accuracy on MNIST #3

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Give 99.28% accuracy on MNIST #3

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30 changes: 22 additions & 8 deletions CNN.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,22 +12,36 @@ def __init__(self):
def build_and_compile_model(self):
if self.modelbuilt:
return
# Add a Convolutional layer
self.model.add(tf.keras.layers.Conv2D(32, (3, 3), input_shape=(28, 28, 1), activation='relu'))
# Add a Max pooling layer
# Add a Convolutional layers and Max pooling layers
self.model.add(tf.keras.layers.Conv2D(32, 5, input_shape=(28, 28, 1), activation='relu'))
self.model.add(tf.keras.layers.MaxPool2D())
# Add the flattened layer

self.model.add(tf.keras.layers.Conv2D(64, 3,activation='relu'))
self.model.add(tf.keras.layers.MaxPool2D())

self.model.add(tf.keras.layers.Conv2D(128, 3,activation='relu'))
self.model.add(tf.keras.layers.MaxPool2D())

self.model.add(tf.keras.layers.Conv2D(256, 2,activation='relu'))
self.model.add(tf.keras.layers.MaxPool2D())

self.model.add(tf.keras.layers.Flatten())
# Add the hidden layer
self.model.add(tf.keras.layers.Dense(512, activation='relu'))
# Adding a dropout layer

# Add the hidden layers and Dropout
self.model.add(tf.keras.layers.Dense(50, activation='relu'))
self.model.add(tf.keras.layers.Dropout(0.2))

self.model.add(tf.keras.layers.Dense(25, activation='relu'))
self.model.add(tf.keras.layers.Dropout(0.2))

# Add the output layer
self.model.add(tf.keras.layers.Dense(10, activation='softmax'))
# Compiling the model
self.model.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"])
self.model.compile(optimizer="nadam", loss="sparse_categorical_crossentropy", metrics=["accuracy"])
self.modelbuilt = True



'''This function loads the Train/Test dataset, trains the model and evaluates it.
It prints the accuracy attained on the test set in the end'''
def train_and_evaluate_model(self):
Expand Down