TimeSeriesDropout_graph = tf.Graph()
with TimeSeriesDropout_graph.as_default():
  keep_prob_input_default = np.array(1.0,dtype='float32')
  keep_prob_input = tf.placeholder_with_default(keep_prob_input_default,shape=())
  keep_prob_state_default = np.array(1.0,dtype='float32')
  keep_prob_state = tf.placeholder_with_default(keep_prob_state_default,shape=())
#   keep_prob_input = 0.5
#   keep_prob_state = 0.5
  X = tf.placeholder(dtype=tf.float32,shape=(None,n_steps,n_inputs))
  y = tf.placeholder(dtype=tf.float32,shape=(None,n_steps,n_outputs))
  multiple_rnn_cell = [tf.keras.layers.SimpleRNNCell(units=n_neurons,activation='relu',dropout=keep_prob_input,recurrent_dropout=keep_prob_state)
                     ,tf.keras.layers.SimpleRNNCell(units=n_neurons//2,activation='relu',dropout=keep_prob_input,recurrent_dropout=keep_prob_state)
                     ,tf.keras.layers.SimpleRNNCell(units=n_neurons//4,activation='relu',dropout=keep_prob_input,recurrent_dropout=keep_prob_state)
                     ,tf.keras.layers.SimpleRNNCell(units=n_neurons//8,activation='relu',dropout=keep_prob_input,recurrent_dropout=keep_prob_state)]
  stacked_rnn_cell = tf.keras.layers.StackedRNNCells(multiple_rnn_cell)
  rnn_output = tf.keras.layers.RNN(stacked_rnn_cell,return_sequences=True,return_state=True)(X)
  outputs_reshaped = tf.reshape(rnn_output[0],(-1,n_neurons//8))
  output = tf.keras.layers.Dense(units=n_outputs)(outputs_reshaped)
  output_stacked = tf.reshape(output,(-1,n_steps,n_outputs))
  loss = tf.reduce_mean(tf.square(y-output_stacked))
  optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
  training_op = optimizer.minimize(loss)
  init = tf.global_variables_initializer()
  saver = tf.train.Saver()

TypeError                                 Traceback (most recent call last)
<ipython-input-29-fd7e00c85d84> in <module>()
      8   X = tf.placeholder(dtype=tf.float32,shape=(None,n_steps,n_inputs))
      9   y = tf.placeholder(dtype=tf.float32,shape=(None,n_steps,n_outputs))
---> 10   multiple_rnn_cell = [tf.keras.layers.SimpleRNNCell(units=n_neurons,activation='relu',dropout=keep_prob_input,recurrent_dropout=keep_prob_state)
     11                      ,tf.keras.layers.SimpleRNNCell(units=n_neurons//2,activation='relu',dropout=keep_prob_input,recurrent_dropout=keep_prob_state)
     12                      ,tf.keras.layers.SimpleRNNCell(units=n_neurons//4,activation='relu',dropout=keep_prob_input,recurrent_dropout=keep_prob_state)

1 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py in __bool__(self)
    651       `TypeError`.
    652     """
--> 653     raise TypeError("Using a `tf.Tensor` as a Python `bool` is not allowed. "
    654                     "Use `if t is not None:` instead of `if t:` to test if a "
    655                     "tensor is defined, and use TensorFlow ops such as "

TypeError: Using a `tf.Tensor` as a Python `bool` is not allowed. Use `if t is not None:` instead of `if t:` to test if a tensor is defined, and use TensorFlow ops such as tf.cond to execute subgraphs conditioned on the value of a tensor.