![]() ![]() #dqn.fit(env, callbacks=callbacks, nb_steps=1000, log_interval=100) # After training is done, we save the final weights one more time. The returned tensors dimension i will correspond to the input dimension permi. fit( env, callbacks= callbacks, nb_steps= 5750000, log_interval= 50000) Permutes the dimensions according to perm. load_weights( dqn_Pong-v0_weights_final.h5f)ĭqn. This function is used to transpose the input tensor. Inherits From: AutoCompositeTensorBijector, Bijector, AutoCompositeTensor ( permutation, axis-1, validateargsFalse, nameNone ) reverse (permutation 2, 1, 0) reverse.forward( -1., 0., 1.) > 1., 0., -1 reverse.inverse( 1., 0. Permutation pattern, does not include the samples dimension. Permutes the rightmost dimension of a Tensor. format( env_name)Ĭallbacks = Ĭallbacks += ĭqn. tf.transpose () is a function provided in TensorFlow. Permute layer is quite picky about its dims argument despite docs clearly saying dims: Tuple of integers. Inherits From: Layer View aliases Compat aliases for. Notice that you can use the built-in Keras callbacks! results_path = '/results/' weights_filename = results_path + 'dqn_log.json'. tf. View source on GitHub Permutes the dimensions of the input according to a given pattern. The following are 30 code examples of ().You can vote up the ones you like or vote down the ones you dont like, and go to the original project or source file by following the links above each example. # Okay, now it's time to learn something! We capture the interrupt exception so that training # can be prematurely aborted. Memory= memory, processor= processor, nb_steps_warmup= 10,ĭqn. ![]() ![]() dqn = DQNAgent( model= model, nb_actions= nb_actions, policy= policy, Sess.# Creating the DQN agent with the specified model, policy, memory etc. # Launch the graph in a session with tf.Session() as sess:įor start, end in zip(range( 0, len(trX), batch_size), range(batch_size, len(trX)+ 1, batch_size)): Train_op = tf.train.RMSPropOptimizer( 0.001, 0.9).minimize(cost) ![]() Py_x, state_size = model(X, W, B, lstm_size)Ĭost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=py_x, labels=Y)) Mnist = input_data.read_data_sets( "MNIST_data/", one_hot= True) # Linear activation # Get the last output return tf.matmul(outputs, W) + B, lstm.state_size # State size to initialize the stat Outputs, _states = rnn.static_rnn(lstm, X_split, dtype=tf.float32) # Get lstm cell output, time_step_size (28) arrays with lstm_size output: (batch_size, lstm_size) att L.Activation(softmax)(att) x L.Permute((2,1))(x) x L.Multiply()(x, att) x tf.reducesum(x. Lstm = rnn.BasicLSTMCell(lstm_size, forget_bias= 1.0, state_is_tuple= True) X_split = tf.split(XR, time_step_size, 0) # split them to time_step_size (28 arrays) # Each array shape: (batch_size, input_vec_size) # Make lstm with lstm_size (each input vector size) XR = tf.reshape(XT, ) # each row has input for each lstm cell (lstm_size=input_vec_size) # XR shape: (time_step_size * batch_size, input_vec_size) XT = tf.transpose(X, ) # permute time_step_size and batch_size # XT shape: (time_step_size, batch_size, input_vec_size) Test_size = 256 def init_weights (shape): return tf.Variable(tf.random_normal(shape, stddev= 0.01))ĭef model (X, W, B, lstm_size): # X, input shape: (batch_size, time_step_size, input_vec_size) # img128 or img256 (batch_size or test_size 256) # each input size = input_vec_size=lstm_size=28 # configuration variables ![]()
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