Tensorflow 错误：形状必须为 2 级，但输入形状为“MatMul”（操作：“MatMul”）为 1 级：[?]、[1024]答案

【问题标题】：Tesnsor Flow error :Shape must be rank 2 but is rank 1 for 'MatMul' (op: 'MatMul') with input shapes: [?], [1024]Tensorflow 错误：形状必须为 2 级，但输入形状为“MatMul”（操作：“MatMul”）为 1 级：[?]、[1024]
【发布时间】：2018-07-08 04:22:36
【问题描述】：

问题是我有一个单一的特征输入并且形状是一维的。我从具有日期和“德里”特征的存储文件中获取数据。这些特征旨在通过系统运行。这里是程序：

import matplotlib.pyplot as plt
import numpy as np
import json,pickle
from sklearn.preprocessing import MinMaxScaler
import tensorflow as tf
sc = MinMaxScaler()
def initialiseDeep(X_train, X_test, y_train, y_test):
  # Model architecture parameters
n_stocks = 1
n_neurons_1 = 1024
n_neurons_2 = 512
n_neurons_3 = 256
n_neurons_4 = 128
n_target = 1
# Placeholder
X = tf.placeholder(dtype=tf.float32,shape=[None])
Y = tf.placeholder(dtype=tf.float32,shape=[None])
# Initializers
sigma = 1
weight_initializer = tf.variance_scaling_initializer(mode="fan_avg", distribution="uniform", scale=sigma)
bias_initializer = tf.zeros_initializer()
# Layer 1: Variables for hidden weights and biases
W_hidden_1 = tf.Variable(weight_initializer([n_stocks, n_neurons_1]))
bias_hidden_1 = tf.Variable(bias_initializer([n_neurons_1]))
# Layer 2: Variables for hidden weights and biases
W_hidden_2 = tf.Variable(weight_initializer([n_neurons_1, n_neurons_2]))
bias_hidden_2 = tf.Variable(bias_initializer([n_neurons_2]))
# Layer 3: Variables for hidden weights and biases
W_hidden_3 = tf.Variable(weight_initializer([n_neurons_2, n_neurons_3]))
bias_hidden_3 = tf.Variable(bias_initializer([n_neurons_3]))
# Layer 4: Variables for hidden weights and biases
W_hidden_4 = tf.Variable(weight_initializer([n_neurons_3, n_neurons_4]))
bias_hidden_4 = tf.Variable(bias_initializer([n_neurons_4]))

# Output layer: Variables for output weights and biases
W_out = tf.Variable(weight_initializer([n_neurons_4, n_target]))
bias_out = tf.Variable(bias_initializer([n_target]))

# Hidden layer
hidden_1 = tf.nn.relu(tf.add(tf.matmul(X, W_hidden_1), bias_hidden_1))
hidden_2 = tf.nn.relu(tf.add(tf.matmul(hidden_1, W_hidden_2), bias_hidden_2))
hidden_3 = tf.nn.relu(tf.add(tf.matmul(hidden_2, W_hidden_3), bias_hidden_3))
hidden_4 = tf.nn.relu(tf.add(tf.matmul(hidden_3, W_hidden_4), bias_hidden_4))

# Output layer (must be transposed)
out = tf.transpose(tf.add(tf.matmul(hidden_4, W_out), bias_out))

# Cost function
mse = tf.reduce_mean(tf.squared_difference(out, Y))
# Optimizer
opt = tf.train.AdamOptimizer().minimize(mse)

# Make Session
net = tf.Session()
# Run initializer
net.run(tf.global_variables_initializer())

# Setup interactive plot
plt.ion()
fig = plt.figure()
ax1 = fig.add_subplot(111)
line1, = ax1.plot(y_test)
line2, = ax1.plot(y_test*0.5)
plt.show()

# Number of epochs and batch size
epochs = 10
batch_size = 15

for e in range(epochs):

    # Shuffle training data
    shuffle_indices = np.random.permutation(np.arange(len(y_train)))
    X_train = X_train[shuffle_indices]
    y_train = y_train[shuffle_indices]

    # Minibatch training
    for i in range(0, len(y_train) // batch_size):
        start = i * batch_size
        batch_x = X_train[start:start + batch_size]
        batch_y = y_train[start:start + batch_size]
        # Run optimizer with batch
        net.run(opt, feed_dict={X: batch_x, Y: batch_y})

        # Show progress
        if np.mod(i, 5) == 0:
            # Prediction
            pred = net.run(out, feed_dict={X: X_test})
            line2.set_ydata(pred)
            plt.title('Epoch ' + str(e) + ', Batch ' + str(i))
            file_name = 'img/epoch_' + str(e) + '_batch_' + str(i) + '.jpg'
            plt.savefig(file_name)
            plt.pause(0.01)
# Print final MSE after Training
mse_final = net.run(mse, feed_dict={X: X_test, Y: y_test})
print(mse_final)


file_Name = "DataSet"
fileObject = open(file_Name,'rb')

Data=pickle.load(fileObject)
JSON=json.loads(Data)
X=[]
y=[]
DataSet=dict(JSON)
for i in range(1,DataSet['Totaldata']+1):
    X.append(int(DataSet[str(i)]['dateFloat']))
    y.append(float(DataSet[str(i)]['Delhi']))
test_len=20
X=np.asarray(X)
y=np.asarray(y)
X_train, X_test, y_train, y_test=np.asarray(X[:len(X)-test_len]),np.asarray(X[len(X)-test_len:]),np.asarray(y[:len(X)-test_len]),np.asarray(y[len(X)-test_len:])
initialiseDeep(X_train, X_test, y_train, y_test)

但是当我运行它时，我得到以下错误：

raise ValueError(err.message)
ValueError: Shape must be rank 2 but is rank 1 for 'MatMul' (op: 'MatMul') 
with input shapes: [?], [1024].

错误的原因是这一行：

hidden_1 = tf.nn.relu(tf.add(tf.matmul(X, W_hidden_1), bias_hidden_1))

谁能帮我解决这个问题，我是深度学习的新手。

【问题讨论】：

标签： python-3.x tensorflow deep-learning

【解决方案1】：

tf.matmul 接受 rank >= 2 的输入。

因此，您可以使用 tf.expand_dims() 将输入的维度扩展为二维。

文档中的一些示例。
if 't' 是 shape [2]
的张量 1. tf.shape(tf.expand_dims(t, 0)) ## 现在的形状是[1, 2]
2. tf.shape(tf.expand_dims(t, 1)) ##现在shape是[2, 1]

参考：https://www.tensorflow.org/api_docs/python/tf/expand_dims

【讨论】：

谢谢。这也有帮助

【解决方案2】：

在张量流的文档中引用：https://www.tensorflow.org/api_docs/python/tf/matmul

这是一个矩阵乘法，它只能发生在至少秩为 2 的矩阵中，并且矩阵 1 中的列数应等于 tf.matmul(matrix1,matrix2) 中矩阵 2 中的行数。

请使用函数 X.shape 和 W_hidden_1.shape 检查两个矩阵的形状。正如我从错误中了解到的那样，这不会遵循上述规则。您也可以使用 tf.transpose 来获得所需的维度。

干杯

【讨论】：

【解决方案3】：

检查你的张量找到 shape = (1222,) 的张量执行以下操作

X=tf.expand_dims(X,1)

它会将其转换为 shape(1222,1)

你很高兴

【讨论】：