From f0173d864af659ae87ea35c8bb9e304fe6cc71fc Mon Sep 17 00:00:00 2001 From: Jan Date: Sat, 2 Jul 2022 13:43:04 -0600 Subject: [PATCH] reorganize creating RNN for easier experiments --- fmda_kf_rnn.ipynb | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/fmda_kf_rnn.ipynb b/fmda_kf_rnn.ipynb index e64c0aa..832e85c 100644 --- a/fmda_kf_rnn.ipynb +++ b/fmda_kf_rnn.ipynb @@ -1390,11 +1390,11 @@ " inputs = tf.keras.Input(shape=input_shape)\n", " # https://stackoverflow.com/questions/43448029/how-can-i-print-the-values-of-keras-tensors\n", " # inputs2 = K.print_tensor(inputs, message='inputs = ') # change allso inputs to inputs2 below, must be used\n", - " x = tf.keras.layers.SimpleRNN(hidden_units,\n", - " activation=activation[0],stateful=stateful)(inputs)\n", - " y = tf.keras.layers.Dense(hidden_units, activation=activation[1])(x)\n", - " outputs = tf.keras.layers.Dense(dense_units, activation=activation[1])(y)\n", - " model = tf.keras.Model(inputs=inputs, outputs=outputs)\n", + " x = inputs\n", + " x = tf.keras.layers.SimpleRNN(hidden_units,activation=activation[0],stateful=stateful)(x)\n", + " # x = tf.keras.layers.Dense(hidden_units, activation=activation[1])(x)\n", + " x = tf.keras.layers.Dense(dense_units, activation=activation[1])(x)\n", + " model = tf.keras.Model(inputs=inputs, outputs=x)\n", " model.compile(loss='mean_squared_error', optimizer='adam')\n", " return model\n", "def create_fit_predict_RNN(hidden_units, dense_units, \n", -- 2.11.4.GIT