Question

我正在尝试使用Python3.5中的Tensorflow在MNIST数据集上运行2层卷积网进行数字识别。输入来自csv文件，我将其作为pandas dataframe读入。 Tensorflow不喜欢pandas数据帧（它不接受输入），因此我将其更改为numpy数组。以下是整个代码 -

sess=tf.InteractiveSession()

train=pd.read_csv('train (1).csv',sep=',',header=0,dtype='float32')
x_train=train.iloc[:,1:]
y_train=train.iloc[:,0]
onehot=OneHotEncoder()
y_train=y_train.reshape(-1,1)
y_train=onehot.fit_transform(y_train)

test=pd.read_csv('test.csv',sep=',',header=0)

x=tf.placeholder(tf.float32, [None, 784])
y_ = tf.placeholder(tf.float32, [None, 10])

W=tf.Variable(tf.zeros([784,10]))
b=tf.Variable(tf.zeros([10]))

y=tf.nn.softmax(tf.matmul(x,W) +b )

def weight_variable(shape):
  initial = tf.truncated_normal(shape, stddev=0.1)
  return tf.Variable(initial)

def bias_variable(shape):
  initial = tf.constant(0.1, shape=shape)
  return tf.Variable(initial)

def conv2d(x, W):
  return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

def max_pool_2x2(x):
  return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                        strides=[1, 2, 2, 1], padding='SAME')

W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])

x_train= x_train.as_matrix()
x_image = tf.reshape(x_train, [-1,28,28,1])

h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)

W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])

h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)

W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])

h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])

y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2

cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y_conv, y_))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

sess.run(tf.initialize_all_variables())

k=0
for i in range(20000):
  x_batch = x_train[k*100:k+100,:]
  y_batch = y_train[k*100:k+100,:]
  if i%100 == 0:
    train_accuracy = accuracy.eval(feed_dict={
        x:x_batch, y_: y_batch, keep_prob: 1.0})
    print("step %d, training accuracy %g"%(i, train_accuracy))
  train_step.run(feed_dict={x: x_batch, y_: y_batch, keep_prob: 0.5})
  k+=1

我得到的错误在precision.eval函数 -

accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

以下是错误消息 -

ValueError: setting an array element with a sequence.

我试着查找错误发生的原因，并且有几个原因。但是，我的输入是数组的形式，我并不熟悉张量，所以我很难理解出了什么问题。

感谢任何帮助。

Python TensorFlow：ValueError：使用序列设置数组元素

0 个答案: