¡Hola, Mundo!

fit A function that let learns a model. It updates the weight of the model by input data and correct answer data. fit( x=None, y=None, batch_size=None, epochs=1, verbose='auto', callbacks=None, validation_split=0.0, validation_data=None, shuffle=True, class_weight=None, sample_weight=None, initial_epoch=0, steps_per_epoch=None, validation_steps=None, validation_batch_size=None, validation_freq=1..

Sequential - To construct with a sequential structure, a stack from the input layer to the output layer. - It lets stack the layers one by one. - It connects several functions such as Wx+b, Sigmoid. model.add() - Adding the layer-an input layer, a hidden layer, and an output layer - You can construct the model using the various layers(Dense, Conv2D, MaxPooling2D, LSTM, etc.). input_shape - Only ..

GradientTape It records intermediate processes of functions, operations on Tape one by one. In other words, computational operation is saved in Tape. Reverse mode automatic differentiation TensorFlow calculates the differentiation of x for the loss by using the Reverse mode automatic differentiation with dx = tape.gradient(loss, x) Backpropagate the differential of x for the loss, repeat the tas..

reduce_sum Computes the sum of elements across dimensions of a tensor. - keepdims=False The rank of the tensor is reduced by 1 for each of the entries in axis A = tf.constant([[[1, 2, 3]], [[4, 5, 6]]]) tf.reduce_sum(A)=21 tf.reduce_sum(A, 0)=[[5 7 9]] tf.reduce_sum(A, 1)=[[1 2 3] [4 5 6]] tf.reduce_sum(A, 2)=[[ 6] [15]] - keepdims=True The reduced dimensions are retained with length 1. tf.reduc..

LSTM(Long Short Term Memory) -The model complemented RNN. -It can over 1000 steps. -It can capture long term dependency. σ is sigmoid(between 0 and 1). C is a highway, h is a national road. Therefore, there is less change to occur vanishing gradient. https://www.coursera.org/learn/cnns-and-rnns/lecture/RJpfH/new-video 1. Input layer that receives 10 inputs Input(shape=(10,)) 2. Words to dense ve..