def add_logits_op_train(self):
if self.cnn_format == "NHWC":
x = tf.transpose(self.state, [0, 2, 3, 1])
else:
x = self.state
self.image_summary = []
w, b, out, summary = conv2d_layer(x,
32, [8, 8], [4, 4],
scope_name="conv1_train",
summary_tag="conv1_out",
activation=tf.nn.relu,
data_format=self.cnn_format)
self.w["wc1"] = w
self.w["bc1"] = b
self.image_summary.append(summary)
w, b, out, summary = conv2d_layer(out,
64, [4, 4], [2, 2],
scope_name="conv2_train",
summary_tag="conv2_out",
activation=tf.nn.relu,
data_format=self.cnn_format)
self.w["wc2"] = w
self.w["bc2"] = b
self.image_summary.append(summary)
w, b, out, summary = conv2d_layer(out,
64, [3, 3], [1, 1],
scope_name="conv3_train",
summary_tag="conv3_out",
activation=tf.nn.relu,
data_format=self.cnn_format)
self.w["wc3"] = w
self.w["bc3"] = b
self.image_summary.append(summary)
shape = out.get_shape().as_list()
out_flat = tf.reshape(
out, [tf.shape(out)[0], 1, shape[1] * shape[2] * shape[3]])
out, state = stateful_lstm(out_flat,
self.num_lstm_layers,
self.lstm_size,
tuple([self.lstm_state_train]),
scope_name="lstm_train")
self.state_output_c = state[0][0]
self.state_output_h = state[0][1]
shape = out.get_shape().as_list()
out = tf.reshape(out, [tf.shape(out)[0], shape[2]])
w, b, out = fully_connected_layer(out,
self.n_actions,
scope_name="out_train",
activation=None)
self.w["wout"] = w
self.w["bout"] = b
self.q_out = out
self.q_action = tf.argmax(self.q_out, axis=1)
def wavenet_logits_target(self):
x = self.get_inputs(self.opens_, self.highs_, self.lows_, self.closes_,
self.volumes_, self.positions_, self.order_prices_,
self.current_prices_, self.time_since_,
self.todays_)
inputs, w, b = temporal_convolution_layer(inputs=x,
output_units=8,
convolution_width=1,
scope='target-CNN-1x1')
self.w_target["wcnn1"] = w
self.w_target["bcnn1"] = b
outputs = lstm_layer(inputs,
self.lengths,
self.lstm_size,
scope="series-lstm-target")
h, w, b = time_distributed_dense_layer(outputs,
128,
scope='target-dense-encode-1',
activation=tf.nn.relu,
reuse=tf.AUTO_REUSE)
self.w_target["wtf1"] = w
self.w_target["btf1"] = b
out, w, b = time_distributed_dense_layer(h,
32,
scope='target-dense-encode-2',
activation=tf.nn.relu,
reuse=tf.AUTO_REUSE)
self.w_target["wtf2"] = w
self.w_target["btf2"] = b
shape = out.get_shape().as_list()
out_flat = tf.reshape(out, [tf.shape(out)[0], 1, shape[1] * shape[2]])
out, state = stateful_lstm(out_flat,
self.num_lstm_layers,
self.lstm_size,
tuple([self.lstm_state_target]),
scope_name="lstm_target")
self.state_output_target_c = state[0][0]
self.state_output_target_h = state[0][1]
shape = out.get_shape().as_list()
out = tf.reshape(out, [tf.shape(out)[0], shape[2]])
out, w, b = fully_connected_layer(out,
self.n_actions,
scope_name='target-dense-encode-2',
activation=None)
self.w_target["wout"] = w
self.w_target["bout"] = b
self.q_target_out = out
self.q_target_action = tf.argmax(self.q_target_out, axis=1)
def wavenet_logits_target(self):
x = self.x
inputs, w, b = time_distributed_dense_layer(
inputs=x,
output_units=self.residual_channels,
activation=tf.nn.tanh,
scope='target-x-proj-encode',
reuse=False)
self.w_target["wf0"] = w
self.w_target["bf0"] = b
skip_outputs = []
conv_inputs = [inputs]
for i, (dilation, filter_width) in enumerate(
zip(self.dilations, self.filter_widths)):
dilated_conv, w, b = temporal_convolution_layer(
inputs=inputs,
output_units=2 * self.residual_channels,
convolution_width=filter_width,
causal=True,
dilation_rate=[dilation],
scope='target-dilated-conv-encode-{}'.format(i),
reuse=tf.AUTO_REUSE)
self.w_target["wc{}".format(i)] = w
self.w_target["wb{}".format(i)] = b
conv_filter, conv_gate = tf.split(dilated_conv, 2, axis=2)
dilated_conv = tf.nn.tanh(conv_filter) * tf.nn.sigmoid(conv_gate)
outputs, w, b = time_distributed_dense_layer(
inputs=dilated_conv,
output_units=self.skip_channels + self.residual_channels,
scope='target-dilated-conv-proj-encode-{}'.format(i),
reuse=tf.AUTO_REUSE)
self.w_target["wtf-{}".format(i)] = w
self.w_target["btf-{}".format(i)] = b
skips, residuals = tf.split(
outputs, [self.skip_channels, self.residual_channels], axis=2)
inputs += residuals
conv_inputs.append(inputs)
skip_outputs.append(skips)
skip_outputs = tf.nn.relu(tf.concat(skip_outputs, axis=2))
h, w, b = time_distributed_dense_layer(skip_outputs,
128,
scope='target-dense-encode-1',
activation=tf.nn.relu,
reuse=tf.AUTO_REUSE)
self.w_target["wtf1"] = w
self.w_target["btf1"] = b
h, w, b = time_distributed_dense_layer(h,
3,
scope='target-dense-encode-2',
activation=tf.nn.relu,
reuse=tf.AUTO_REUSE)
self.w_target["wtf2"] = w
self.w_target["btf2"] = b
s = h.get_shape().as_list()
out_flat = tf.reshape(h, [-1, reduce(lambda x, y: x * y, s[1:])])
h, w, b = fully_connected_layer(out_flat,
128,
scope_name='target-dense-encode-1',
activation=tf.nn.relu)
self.w_target["wf1"] = w
self.w_target["bf1"] = b
out, w, b = fully_connected_layer(h,
self.n_actions,
scope_name='target-dense-encode-2',
activation=None)
self.w_target["wout"] = w
self.w_target["bout"] = b
self.q_target_out = out
self.q_target_action = tf.argmax(self.q_target_out, axis=1)