dropout

I've trained the following model for some timeseries in Keras: input_layer = Input(batch_shape=(56, 3864)) first_layer = Dense(24, input_dim=28, activation='relu', activity_regularizer=None, kernel_regularizer=None)(input_layer) first_layer = Dropout(0.3)(first_layer) second_layer = Dense(12, activation='relu')(first_layer) second_layer = Dropout(0.3)(second_layer) out = Dense(56)(second_layer) model_1 = Model(input_layer, out) Then I defined a new model with the trained layers of model_1 and added dropout layers with a different rate, drp , to it: input_2 = Input(batch_shape=(56, 3864)) first

I wish to view the final output of training a tf.keras model. In this case it would be an array of predictions from the softmax function, e.g. [0,0,0,1,0,1]. Other threads on here have suggested using model.predict(training_data), but this won't work for my situation since I am using dropout at training and validation, so neurons are randomly dropped and predicting again with the same data will give a different result. def get_model(): inputs = tf.keras.layers.Input(shape=(input_dims,)) x = tf.keras.layers.Dropout(rate=dropout_rate)(inputs, training=True) x = tf.keras.layers.Dense(units=29,

From the Keras documentation: dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. recurrent_dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state. Can anyone point to where on the image below each dropout happens? I suggest taking a look at (the first part of) this paper . Regular dropout is applied on the inputs and/or the outputs, meaning the vertical arrows from x_t and to h_t . In your case, if you add it as an argument to your layer, it will mask the inputs; you can add a