3
これはGANを訓練するテンソルフローのコードです。偽のビデオとオリジナルのビデオを区別できるようにトレーニングしています。私は流れの上にスタックを回避するために、コードの関連は重要でない部分を持っているほとんどのコードのエラーTensorflow：LSTMのvariable_scopeでの値のエラー
X = tf.placeholder(tf.float32, shape=[None, 28, 28])
D_W1 = tf.Variable(xavier_init([1024, 128]))
D_b1 = tf.Variable(tf.zeros(shape=[128]))
D_W2 = tf.Variable(xavier_init([128, 1]))
D_b2 = tf.Variable(tf.zeros(shape=[1]))
theta_D = [D_W1, D_W2, D_b1, D_b2]
rnn_size = 1024
rnn_layer = 2
Z = tf.placeholder(tf.float32, shape=[None, 100])
G_W1 = tf.Variable(xavier_init([100, 128]))
G_b1 = tf.Variable(tf.zeros(shape=[128]))
G_W2 = tf.Variable(xavier_init([128, 784]))
G_b2 = tf.Variable(tf.zeros(shape=[784]))
theta_G = [G_W1, G_W2, G_b1, G_b2]
def sample_Z(m, n):
return np.random.uniform(-1., 1., size=[m, n])
def generator(z):
G_h1 = tf.nn.relu(tf.matmul(z, G_W1) + G_b1)
G_log_prob = tf.matmul(G_h1, G_W2) + G_b2
G_prob = tf.nn.sigmoid(G_log_prob)
G_prob = tf.reshape(G_prob, [-1,28, 28])
return G_prob
def discriminator(x):
x = [tf.squeeze(t, [1]) for t in tf.split(x, 28, 1)]
# with tf.variable_scope('cell_def'):
stacked_rnn1 = []
for iiLyr1 in range(rnn_layer):
stacked_rnn1.append(tf.nn.rnn_cell.BasicLSTMCell(num_units=rnn_size, state_is_tuple=True))
lstm_multi_fw_cell = tf.contrib.rnn.MultiRNNCell(cells=stacked_rnn1)
# with tf.variable_scope('rnn_def'):
dec_outputs, dec_state = tf.contrib.rnn.static_rnn(
lstm_multi_fw_cell, x, dtype=tf.float32)
D_h1 = tf.nn.relu(tf.matmul(dec_outputs[-1], D_W1) + D_b1)
D_logit = tf.matmul(D_h1, D_W2) + D_b2
D_prob = tf.nn.sigmoid(D_logit)
return D_prob, D_logit
G_sample = generator(Z)
print(G_sample.get_shape())
print(X.get_shape())
D_real, D_logit_real = discriminator(X)
D_fake, D_logit_fake = discriminator(G_sample)
D_loss = -tf.reduce_mean(tf.log(D_real) + tf.log(1. - D_fake))
G_loss = -tf.reduce_mean(tf.log(D_fake))
summary_d = tf.summary.histogram('D_loss histogram', D_loss)
summary_g = tf.summary.histogram('D_loss histogram', G_loss)
summary_s = tf.summary.scalar('D_loss scalar', D_loss)
summary_s1 = tf.summary.scalar('scalar scalar', G_loss)
# Add image summary
summary_op = tf.summary.image("plot", image)
D_solver = tf.train.AdamOptimizer().minimize(D_loss, var_list=theta_D)
G_solver = tf.train.AdamOptimizer().minimize(G_loss, var_list=theta_G)
mb_size = 128
Z_dim = 100
mnist = input_data.read_data_sets('../../MNIST_data', one_hot=True)
# merged_summary_op = tf.summary.merge_all()
sess = tf.Session()
saver = tf.train.Saver()
writer1 = tf.summary.FileWriter('log/log-sample1', sess.graph)
writer2 = tf.summary.FileWriter('log/log-sample2', sess.graph)
sess.run(tf.global_variables_initializer())
if not os.path.exists('out/'):
os.makedirs('out/')
i = 0
with tf.variable_scope("myrnn") as scope:
for it in range(5000):
X_mb, _ = mnist.train.next_batch(mb_size)
X_mb = tf.reshape(X_mb, [mb_size, -1, 28])
_, D_loss_curr = sess.run([D_solver, D_loss], feed_dict={X: X_mb, Z: sample_Z(mb_size, Z_dim)})
_, G_loss_curr = sess.run([G_solver, G_loss], feed_dict={Z: sample_Z(mb_size, Z_dim)})
summary_str, eded = sess.run([summary_d, summary_s], feed_dict={X: X_mb, Z: sample_Z(mb_size, Z_dim)})
writer1.add_summary(summary_str, it)
writer1.add_summary(eded, it)
summary_str1, eded1 = sess.run([summary_g, summary_s1], feed_dict={X: X_mb, Z: sample_Z(mb_size, Z_dim)})
writer2.add_summary(summary_str1, it)
writer2.add_summary(eded1, it)
if it % 1000 == 0:
print('Iter: {}'.format(it))
print('D loss: {:.4}'. format(D_loss_curr))
print('G_loss: {:.4}'.format(G_loss_curr))
print()
save_path = saver.save(sess, "tmp/model.ckpt")
writer1.close()
writer2.close()
`
後、私はこのコード助けてくださいを実行すると、エラーです。
Traceback (most recent call last):
File "/Users/tulsijain/Desktop/Deep Learning Practise/GAN/vanila.py", line 104, in <module>
D_fake, D_logit_fake = discriminator(G_sample)
File "/Users/tulsijain/Desktop/Deep Learning Practise/GAN/vanila.py", line 64, in discriminator
lstm_multi_fw_cell, x, dtype=tf.float32)
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tensorflow/python/ops/rnn.py", line 1212, in static_rnn
(output, state) = call_cell()
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tensorflow/python/ops/rnn.py", line 1199, in <lambda>
call_cell = lambda: cell(input_, state)
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tensorflow/python/ops/rnn_cell_impl.py", line 180, in __call__
return super(RNNCell, self).__call__(inputs, state)
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tensorflow/python/layers/base.py", line 441, in __call__
outputs = self.call(inputs, *args, **kwargs)
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tensorflow/python/ops/rnn_cell_impl.py", line 916, in call
cur_inp, new_state = cell(cur_inp, cur_state)
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tensorflow/python/ops/rnn_cell_impl.py", line 180, in __call__
return super(RNNCell, self).__call__(inputs, state)
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tensorflow/python/layers/base.py", line 441, in __call__
outputs = self.call(inputs, *args, **kwargs)
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tensorflow/python/ops/rnn_cell_impl.py", line 383, in call
concat = _linear([inputs, h], 4 * self._num_units, True)
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tensorflow/python/ops/rnn_cell_impl.py", line 1017, in _linear
initializer=kernel_initializer)
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tensorflow/python/ops/variable_scope.py", line 1065, in get_variable
use_resource=use_resource, custom_getter=custom_getter)
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tensorflow/python/ops/variable_scope.py", line 962, in get_variable
use_resource=use_resource, custom_getter=custom_getter)
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tensorflow/python/ops/variable_scope.py", line 360, in get_variable
validate_shape=validate_shape, use_resource=use_resource)
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tensorflow/python/ops/variable_scope.py", line 1405, in wrapped_custom_getter
*args, **kwargs)
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tensorflow/python/ops/rnn_cell_impl.py", line 183, in _rnn_get_variable
variable = getter(*args, **kwargs)
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tensorflow/python/ops/rnn_cell_impl.py", line 183, in _rnn_get_variable
variable = getter(*args, **kwargs)
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tensorflow/python/ops/variable_scope.py", line 352, in _true_getter
use_resource=use_resource)
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tensorflow/python/ops/variable_scope.py", line 664, in _get_single_variable
name, "".join(traceback.format_list(tb))))
ValueError: Variable rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel already exists, disallowed. Did you mean to set reuse=True in VarScope? Originally defined at:
File "/Users/tulsijain/Desktop/Deep Learning Practise/GAN/vanila.py", line 64, in discriminator
lstm_multi_fw_cell, x, dtype=tf.float32)
File "/Users/tulsijain/Desktop/Deep Learning Practise/GAN/vanila.py", line 103, in <module>
D_real, D_logit_real = discriminator(X)
これはGANです。私は、発電機とディスクリミネータを訓練するためにMNISTデータを使用しています。
D_fake、D_logit_fake =弁別（G_sample）の部分は、エラー – Tulsi