Question

请有人帮我解决这个错误，我已经给出了巨大的代码，以便更好地理解。该代码取自Github，并且遇到很多错误。 我认为这段代码只会给我两个错误，以下是我在下面///和///之间标记的两个块，您可以忽略其余的代码。 Github链接：https://github.com/aqibsaeed/Human-Activity-Recognition-using-CNN/blob/master/Activity%20Detection.ipynb

    import pandas as pd
    import numpy as np
    import matplotlib.pyplot as plt
    from scipy import stats
    import tensorflow.compat.v1 as tf


dataset = read_data(r"C:\Users\NEW\Desktop\Internship\Human Action
Recognition\Dataset\WISDM_ar_v1.1\Action.txt")
dataset.dropna(axis=0, how='any', inplace= True)
dataset['x-axis'] = feature_normalize(dataset['x-axis'])
dataset['y-axis'] = feature_normalize(dataset['y-axis'])
dataset['z-axis'] = feature_normalize(dataset['z-axis'])

for activity in np.unique(dataset["activity"]):
    subset = dataset[dataset["activity"] == activity][:180]
    plot_activity(activity,subset)

segments, labels = segment_signal(dataset)
labels = np.asarray(pd.get_dummies(labels), dtype = np.int8)
reshaped_segments = segments.reshape(len(segments), 1,90, 3)

train_test_split = np.random.rand(len(reshaped_segments)) < 0.70
train_x = reshaped_segments[train_test_split]
train_y = labels[train_test_split]
test_x = reshaped_segments[~train_test_split]
test_y = labels[~train_test_split]

input_height = 1
input_width = 90
num_labels = 6
num_channels = 3

batch_size = 10
kernel_size = 60
depth = 60
num_hidden = 1000

learning_rate = 0.0001
training_epochs = 8

total_batches = train_x.shape[0] // batch_size

tf.disable_v2_behavior()
///X = tf.placeholder(shape=[None,input_height,input_width,num_channels],dtype = tf.float32)
Y = tf.placeholder(shape=[None,num_labels],dtype=tf.float32)
c = apply_depthwise_conv(X,kernel_size,num_channels,depth)
p = apply_max_pool(c,20,2)
c = apply_depthwise_conv(p,6,depth*num_channels,depth//10)**///

shape = c.get_shape().as_list()
c_flat = tf.reshape(c, [-1, shape[1] * shape[2] * shape[3]])

f_weights_l1 = weight_variable([shape[1] * shape[2] * depth * num_channels * (depth//10), num_hidden])
f_biases_l1 = bias_variable([num_hidden])
f = tf.nn.tanh(tf.add(tf.matmul(c_flat, f_weights_l1),f_biases_l1))

out_weights = weight_variable([num_hidden, num_labels])
out_biases = bias_variable([num_labels])
y_ = tf.nn.softmax(tf.matmul(f, out_weights) + out_biases)

loss = -tf.reduce_sum(Y * tf.log(y_))
optimizer = tf.train.GradientDescentOptimizer(learning_rate = learning_rate).minimize(loss)

correct_prediction = tf.equal(tf.argmax(y_,1), tf.argmax(Y,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

cost_history = np.empty(shape=[1],dtype=float)

///with tf.Session() as session:
    tf.global_variables_initializer().run()
    for epoch in range(training_epochs):
        for b in range(total_batches):    
            offset = (b * batch_size) % (train_y.shape[0] - batch_size)
            batch_x = train_x[offset:(offset + batch_size), :, :, :]
            batch_y = train_y[offset:(offset + batch_size), :]
            c = session.run([optimizer, loss],feed_dict={X: batch_x, Y : batch_y})
            cost_history = np.append(cost_history,c)
        print("Epoch: ",epoch," Training Loss: ",c," Training Accuracy: ",session.run(accuracy, feed_dict={X: train_x, Y: train_y}))
    print("Testing Accuracy:", session.run(accuracy, feed_dict={X: test_x, Y: test_y}))///

错误： ValueError跟踪（最近一次通话）在

     10             c = session.run([optimizer, loss],feed_dict={X: batch_x, Y : batch_y})
     11             cost_history = np.append(cost_history,c)
---> 12         print("Epoch: ",epoch," Training Loss: ",c," Training Accuracy: ",session.run(accuracy, feed_dict={X: train_x, Y: train_y}))
     13     print("Testing Accuracy:", session.run(accuracy, feed_dict={X: test_x, Y: test_y}))

~\anaconda3\envs\tensorflow-sessions\lib\site-packages\tensorflow_core\python\client\session.py in run(self, fetches, feed_dict, options, run_metadata)
    958     try:
    959       result = self._run(None, fetches, feed_dict, options_ptr,
--> 960                          run_metadata_ptr)
    961       if run_metadata:
    962         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

~\anaconda3\envs\tensorflow-sessions\lib\site-packages\tensorflow_core\python\client\session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
   1157                 'Cannot feed value of shape %r for Tensor %r, '
   1158                 'which has shape %r' %
-> 1159                 (np_val.shape, subfeed_t.name, str(subfeed_t.get_shape())))
   1160           if not self.graph.is_feedable(subfeed_t):
   1161             raise ValueError('Tensor %s may not be fed.' % subfeed_t)

ValueError：无法为形状为（（,, 6））的张量'Placeholder_3：0'输入形状（0，0）的值

Answer 1

（0，0）的形状表示没有数据进入网络，无论您执行多少张量操作都不会存在。

ValueError：无法为张量为'（？，6）'的张量'Placeholder_3：0'输入形状（0，0）的值

1 个答案: