john-adeojo · January 22, 2021 00:21
diff --git a/gistfile1.txt b/gistfile1.txt
 # Build sequential neural network

 model = keras.models.Sequential()  # Keras sequential model is composed of a single stack of layers connected sequentially
 model.add(keras.layers.Input(shape=(784)))  # Input layer is a 1D array (no parameters, first layer so specify input shape, not including batch size just size of instances)
 model.add(keras.layers.Dense(300, activation="relu"))  # Dense hidden layer with 300 neurons and ReLU activation fucntion ( each dense layer manages it's own weight matrix and bias vector)
 model.add(keras.layers.Dense(100, activation="relu"))  # Second Dense hidden layer of 100 neurons
 model.add(keras.layers.Dense(10, activation="softmax"))  # Final layer is a dense output layer of 10 neurons with softmax activation function (there are 10 exxclusive classes)

 # Displays all the model's layers 
 model.summary()
	# Build sequential neural network

	model = keras.models.Sequential() # Keras sequential model is composed of a single stack of layers connected sequentially
	model.add(keras.layers.Input(shape=(784))) # Input layer is a 1D array (no parameters, first layer so specify input shape, not including batch size just size of instances)
	model.add(keras.layers.Dense(300, activation="relu")) # Dense hidden layer with 300 neurons and ReLU activation fucntion ( each dense layer manages it's own weight matrix and bias vector)
	model.add(keras.layers.Dense(100, activation="relu")) # Second Dense hidden layer of 100 neurons
	model.add(keras.layers.Dense(10, activation="softmax")) # Final layer is a dense output layer of 10 neurons with softmax activation function (there are 10 exxclusive classes)

	# Displays all the model's layers
	model.summary()