Q&A
下記のようにtrain.pyがあります
その上で下記コードを実行するとエラーが出ます。
原理的には合ってるはずなのですが、何故下記エラーが出てしますのでしょうか？
修正させていただきました。回答よろし願いします。
import sys import pickle import numpy as np import tensorflow as tf import os import matplotlib.pyplot as plt import scipy.misc import time from train import DCGAN from layer import show_all_variables def unpickle(file): fp = open(file, 'rb') if sys.version_info.major == 2: data = pickle.load(fp) elif sys.version_info.major == 3: data = pickle.load(fp, encoding='latin-1') fp.close() return data test= unpickle("train_image.pickle") X_image=np.array(test)/127.5 - 1 run_config = tf.ConfigProto() run_config.gpu_options.allow_growth=True with tf.Session(config=run_config) as sess: dcgan = DCGAN(sess, X_image=X_image, epochs=10, step=20) show_all_variables() dcgan.train() --------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-16-fb64556ff7b2> in <module>() 3 4 with tf.Session(config=run_config) as sess: ----> 5 dcgan = DCGAN(sess, X_image=X_image, epochs=10, step=20) 6 show_all_variables() 7 dcgan.train() ~/DCGAN-tensorflow/train.py in __init__(self, sess, X_image, epochs, step) 27 self.step = step 28 ---> 29 self.build_model() 30 31 ~/DCGAN-tensorflow/train.py in build_model(self) 35 batch_size=64 36 with tf.variable_scope("discriminator") as scope: ---> 37 h0 = lrelu(conv2d(image, 64, name='d_h0_conv')) 38 h1 = lrelu(batch_norm(conv2d(h0, 128, name='d_h1_conv'),'d_bn1')) 39 h2 = lrelu(batch_norm(conv2d(h1, 256, name='d_h2_conv'),'d_bn2')) NameError: name 'generator' is not defined
train.py # coding: utf-8 # In[1]: import sys import pickle import numpy as np import tensorflow as tf import os import matplotlib.pyplot as plt import scipy.misc import time from layer import * # In[2]: class DCGAN(object): def __init__(self, sess, X_image, epochs, step): self.sess = sess self.X_image=X_image self.epochs = epochs self.step = step self.build_model() def discriminator(self, image): batch_size=64 with tf.variable_scope("discriminator") as scope: h0 = lrelu(conv2d(image, 64, name='d_h0_conv')) h1 = lrelu(batch_norm(conv2d(h0, 128, name='d_h1_conv'),'d_bn1')) h2 = lrelu(batch_norm(conv2d(h1, 256, name='d_h2_conv'),'d_bn2')) h3 = lrelu(batch_norm(conv2d(h2, 512, name='d_h3_conv'),'d_bn3')) # shape=(batch_size, 64, 64, 3) h4 = linear_d(tf.reshape(h3, [batch_size, -1]),2,'d_h4_lin') return h4 # shape=(batch_size, 64, 64, 3) def generator(self, z_): batch_size=64 with tf.variable_scope("generator") as scope: # project `z` and reshape z, h0_w, h0_b = linear(z_, 64*8*4*4, 'g_h0_lin',with_w=True) h0 = tf.nn.relu(batch_norm(tf.reshape(z, [-1, 4, 4, 64*8]), 'g_bn0')) h1, h1_w, h1_b = deconv2d(h0, [batch_size, 8, 8, 64*4], name='g_h1', with_w=True) h1 = tf.nn.relu(batch_norm(h1, 'g_bn1')) h2, h2_w, h2_b = deconv2d(h1, [batch_size, 16, 16, 64*2], name='g_h2', with_w=True) h2 = tf.nn.relu(batch_norm(h2, 'g_bn2')) h3, h3_w, h3_b = deconv2d(h2, [batch_size, 32, 32, 64*1], name='g_h3', with_w=True) h3 = tf.nn.relu(batch_norm(h3, 'g_bn3')) h4, h4_w, h4_b = deconv2d(h3, [batch_size, 64, 64, 3], name='g_h4', with_w=True) return tf.nn.tanh(h4) #shape=(batch_size, 64, 64, 3) def sampler(self, z_):# shape=(batch_size, 64, 64, 3) batch_size=64 with tf.variable_scope("generator") as scope: # project `z` and reshape z= linear(z_, 64*8*4*4,'g_h0_lin') h0 = tf.nn.relu(batch_norm(tf.reshape(z, [-1, 4, 4, 64*8]),'g_bn0',train=False)) h1 = deconv2d(h0, [batch_size, 8, 8, 64*4], name='g_h1') h1 = tf.nn.relu(batch_norm(h1,'g_bn1',train=False)) h2 = deconv2d(h1, [batch_size, 16, 16, 64*2], name='g_h2') h2 = tf.nn.relu(batch_norm(h2,'g_bn2',train=False)) h3 = deconv2d(h2, [batch_size, 32, 32, 64*1], name='g_h3') h3 = tf.nn.relu(batch_norm(h3,'g_bn3',train=False)) h4 = deconv2d(h3, [batch_size, 64, 64, 3], name='g_h4') return tf.nn.tanh(h4) #shape=(batch_size, 64, 64, 3) def build_model(self): self.z = tf.placeholder(tf.float32, [64, 100]) self.image = tf.placeholder(tf.float32, [64, 64, 64, 3]) self.G=self.generator(self.z) #G(z) self.D_logits = self.discriminator(self.image) #D(x) self.sampler = self.sampler(self.z) self.D_logits_ = self.discriminator(self.G) #D(G(z)) batch_label=64 self.d_loss_real = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=self.D_logits, labels=tf.ones([batch_label], dtype=tf.int64))) self.d_loss_fake = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=self.D_logits_, labels=tf.zeros([batch_label], dtype=tf.int64))) self.g_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=self.D_logits_, labels=tf.ones([batch_label], dtype=tf.int64))) self.d_loss = self.d_loss_real + self.d_loss_fake self.d_vars = [var for var in tf.trainable_variables() if 'd_' in var.name] self.g_vars = [var for var in tf.trainable_variables() if 'g_' in var.name] self.saver=tf.train.Saver() def train(self): g_optim = tf.train.AdamOptimizer(learning_rate=0.0002, beta1=0.5).minimize(self.g_loss, var_list=self.g_vars) d_optim = tf.train.AdamOptimizer(learning_rate=0.0002, beta1=0.5).minimize(self.d_loss, var_list=self.d_vars) tf.global_variables_initializer().run() sample_z = np.random.uniform(-1, 1, size=(64, 100)) batch_z = np.random.uniform(-1, 1, [64, 100]) sample_files = self.X_image[0:64] sample = [sample_file for sample_file in sample_files] sample_images = np.array(sample).astype(np.float32) counter=1 start_time=time.time() for epoch in range(self.epochs): batch_idxs= min (len(self.X_image), np.inf) // 64 for idx in range (0, batch_idxs): bacth_files= self.X_image[idx*64:(idx+1)*64] batch = [batch_file for batch_file in bacth_files] batch_images = np.array(batch).astype(np.float32) self.sess.run(d_optim, feed_dict = {self.z: batch_z, self.image: batch_images}) self.sess.run(g_optim, feed_dict = {self.z: batch_z}) # Run g_optim twice to realize loss value self.sess.run(g_optim, feed_dict = {self.z: batch_z}) errD_fake = self.d_loss_fake.eval({self.z: batch_z }) errD_real = self.d_loss_real.eval({self.image: batch_images}) errG = self.g_loss.eval({self.z: batch_z}) counter += 1 print("Epoch: [%2d] [%4d/%4d] time:%4.4f, d_loss: %.8f, g_loss: %.8f" % (epoch, idx, batch_idxs, time.time()-start_time, errD_fake+errD_real, errG)) if np.mod(counter, self.step)==1: samples, d_loss_sample, g_loss_sample = sess.run([self.sampler, self.d_loss, self.g_loss], feed_dict={self.z: sample_z, self.image: sample_images}) print("[Sample] d_loss:%.8f, g_loss:%.8f" % (d_loss_sample, g_loss_sample)) col=8 rows=[] for i in range(8): rows.append(np.hstack(samples[col * i + 0:col * i + col])) vnari=np.vstack(rows) plt.imshow(vnari) plt.show()
回答1件
0
ベストアンサー
NameError: name 'generator' is not defined
このエラーが出るよりも前に別のエラーがたくさん含まれています。まず、動作するコードを提示してください。
想像で修正したコードは以下の様になります。
python
1class staff: 2 def __init__(self,sess): 3 self.sess = sess # 変数名修正 4 self.build() 5 6 def build(self): # 引数selfが必須 7 self.G = self.generator(self.sess) # self.zは存在しないので変更 8 9 def generator(self, z) 10 return z
文法的にはこれで動作はします。
投稿2017/08/20 00:39
総合スコア1845
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このような回答には修正を依頼しましょう。
2017/08/20 01:35