TensorFlow#

Layers#

Module: tf.keras.layers

  • InputLayer: tf.keras.layers.InputLayer(input_shape=(10,))

  • Dense: tf.keras.layers.Dense(units, activation=None)

  • Flatten: tf.keras.layers.Flatten()

  • Dropout: tf.keras.layers.Dropout(rate)

  • Conv2D: tf.keras.layers.Conv2D(filters, kernel_size, activation=None, input_shape)

    • input_shape is expected to be a 3D tuple, with the last dimension being the colour channels, e.g. (28,28,3) for 28 x 28 pixels and 3 colour channels (RGB). An alpha channel would probably be a fourth channel?

  • MaxPooling2D: tf.keras.layers.MaxPooling2D(pool_size=(2, 2))

Activation Functions#

Module: tf.keras.activations

The activation functions are also available as individual layers, e.g. when you would like the model to output logits and would like to build a separate probability model with an additional softmax layer appended.

  • ReLu: "relu", tf.keras.activations.relu

  • Softmax: "softmax", tf.keras.activations.softmax

  • Sigmoid: "sigmoid", tf.keras.activations.sigmoid

    • Normally, the number of output neurons should match the number of classes in a classification problem. One exception is binary classification, where it is possible to use one single output neuron with sigmoid activation.

Optimizers#

Module: tf.keras.optimizers

  • SGD: optimizer='sgd', tf.keras.optimizers.SGD(learning_rate=0.01, momentum=0.0)

  • Adam: optimizer='adam', tf.keras.optimizers.Adam(learning_rate=0.001)

  • RMSProp: tf.keras.optimizers.RMSprop(learning_rate=0.001)

Loss Functions#

Module: tf.keras.losses

  • Binary Crossentropy: tf.keras.losses.BinaryCrossentropy(from_logits=False)

  • Categorical Crossentropy: tf.keras.losses.CategoricalCrossentropy(from_logits=False)

  • Sparse Categorical Crossentropy: tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False)

  • Mean Squared Error: tf.keras.losses.MeanSquaredError

Metrics#

  • "accuracy"

Inspection / Information about the Model#

  • model.summary()

  • model.input_shape

  • model.output_shape

  • model.layers

Regularisation#

Module: tf.keras.regularizers

Treatment of Inputs#