action_ = np.array([actions[i]])
state_ = np.array([states[i]])
# print "Action: " + str([actions[i]])
# tmp_action = f(states[i])
# print ("Ation diff: " , tmp_action, action_)
experience.insert(state_, action_, state_, np.array([0]))
# tmp_state_ = scale_state(experience._state_history[experience.samples()-1], state_bounds)
# tmp_action_ = scale_action(experience._action_history[experience.samples()-1], action_bounds)
# tmp_action = f(tmp_state_)
# print ("State diff: " , tmp_state_, states[i])
# print ("Ation diff2: " , tmp_action, action_)
# model = Sequential()
# 2 inputs, 10 neurons in 1 hidden layer, with tanh activation and dropout
input = Input(shape=[1])
input.trainable = True
print("Input ", input)
network = Dense(128, init='uniform')(input)
print("Network: ", network)
network = Activation('relu')(network)
network = Dense(64, init='uniform')(network)
network = Activation('relu')(network)
# 1 output, linear activation
network = Dense(1, init='uniform')(network)
network = Activation('linear')(network)
model = Model(input=input, output=network)
sgd = SGD(lr=0.01, momentum=0.9)
print("Clipping: ", sgd.decay)
model.compile(loss='mse', optimizer=sgd)
print("Loss ", model.total_loss)
weights = [input] + model.trainable_weights # weight tensors
def __init__(self, n_in, n_out, state_bounds, action_bounds, reward_bound, settings_):
super(DeepCNNKeras,self).__init__(n_in, n_out, state_bounds, action_bounds, reward_bound, settings_)
### Apparently after the first layer the patch axis is left out for most of the Keras stuff...
input = Input(shape=(self._state_length,))
input.trainable = True
print ("Input ", input)
## Custom slice layer, Keras does not have this layer type...
taskFeatures = Lambda(lambda x: x[:,0:self._settings['num_terrain_features']], output_shape=(self._settings['num_terrain_features'],))(input)
# taskFeatures = Lambda(lambda x: x[:,0:self._settings['num_terrain_features']])(input)
characterFeatures = Lambda(lambda x: x[:,self._settings['num_terrain_features']:self._state_length], output_shape=(self._state_length-self._settings['num_terrain_features'],))(input)
# characterFeatures = Reshape((-1, self._state_length-self._settings['num_terrain_features']))(characterFeatures)
# characterFeatures = Lambda(lambda x: x[:,self._settings['num_terrain_features']:self._state_length], output_shape=(1,))(input)
# print("TaskFeature shape: ", taskFeatures.output_shape)
network = Reshape((self._settings['num_terrain_features'], 1))(taskFeatures)
network = Conv1D(filters=16, kernel_size=8)(network)
network = Activation('relu')(network)
network = Conv1D(filters=16, kernel_size=8)(network)
network = Activation('relu')(network)
self._critic_task_part = network
network = Flatten()(network)
# characterFeatures = Flatten()(characterFeatures)
# network = Concatenate(axis=1)([network, characterFeatures])
## network.shape should == (None, self._settings['num_terrain_features']) and
## characterFeatures.shape should == (None, state_length-self._settings['num_terrain_features'])
print ("characterFeatures ", characterFeatures)
print ("network ", network)
network = Concatenate(axis=1)([network, characterFeatures])
network = Dense(128, init='uniform')(network)
print ("Network: ", network)
network = Activation('relu')(network)
network = Dense(64, init='uniform')(network)
network = Activation('relu')(network)
network = Dense(32, init='uniform')(network)
network = Activation('relu')(network)
# 1 output, linear activation
network = Dense(1, init='uniform')(network)
network = Activation('linear')(network)
self._critic = Model(input=input, output=network)
inputAct = Input(shape=(self._state_length, ))
inputAct.trainable = True
print ("Input ", inputAct)
## Custom slice layer, Keras does not have this layer type...
taskFeaturesAct = Lambda(lambda x: x[:,0:self._settings['num_terrain_features']], output_shape=(self._settings['num_terrain_features'],))(inputAct)
characterFeaturesAct = Lambda(lambda x: x[:,self._settings['num_terrain_features']:self._state_length], output_shape=(self._state_length-self._settings['num_terrain_features'], ))(inputAct)
## Keras/Tensorflow likes the channels to be at the end
networkAct = Reshape((self._settings['num_terrain_features'], 1))(taskFeaturesAct)
networkAct = Conv1D(filters=16, kernel_size=8)(networkAct)
networkAct = Activation('relu')(networkAct)
networkAct = Conv1D(filters=16, kernel_size=8)(networkAct)
networkAct = Activation('relu')(networkAct)
self._actor_task_part = networkAct
networkAct = Flatten()(networkAct)
networkAct = Concatenate(axis=1)([networkAct, characterFeaturesAct])
networkAct = Dense(128, init='uniform')(networkAct)
print ("Network: ", networkAct)
networkAct = Activation('relu')(networkAct)
networkAct = Dense(64, init='uniform')(networkAct)
networkAct = Activation('relu')(networkAct)
networkAct = Dense(32, init='uniform')(networkAct)
networkAct = Activation('relu')(networkAct)
# 1 output, linear activation
networkAct = Dense(self._action_length, init='uniform')(networkAct)
networkAct = Activation('linear')(networkAct)
self._actor = Model(input=inputAct, output=networkAct)
video_input = Input(shape=(maxlen, vecin.shape[1]), name='video_input')
video_embed = Dropout(dropout_rate)(Dense(128, input_shape=(maxlen, vecin.shape[1]), activation='softmax', name='video_dense')(video_input))
# kernel_regularizer=regularizers.l2(0.01), activation='softmax', name='video_dense')(video_input))
texts_input = Input(shape=(maxlen, len(word_indices)), name='texts_input')
texts_lstm = LSTM(lstm_size, return_sequences=True, input_shape=(maxlen, len(word_indices)),
dropout=dropout_rate, recurrent_dropout=dropout_rate, name='LSTM_input')(texts_input)
layer_0 = Dropout(dropout_rate)(concatenate(
[topic_embed, video_embed, texts_lstm], axis=2, name='merged_input'))
layer_1 = LSTM(lstm_size, return_sequences=False, input_shape=(lstm_size,),
dropout=dropout_rate, recurrent_dropout=dropout_rate, name='meta-LSTM')(layer_0)
layer_2 = Dense(len(word_indices), name='word_selector')(layer_1)
output = Activation('softmax', name='output')(layer_2)
topic_input.trainable = True
topic_embed.trainable = True
video_input.trainable = True
video_embed.trainable = True
texts_input.trainable = True
texts_lstm.trainable = True
layer_1.trainable = True
model_video = Model(inputs=[topic_input, video_input, texts_input], outputs=[output])
model_video.compile(loss='categorical_crossentropy', optimizer=optimiser)
params = np.repeat(np.atleast_2d(video_param), maxlen, axis=0)
Writing = True
while Writing or Loop:
model_video.load_weights(brain_file)
