ValueError：无法为张量为'（？，500）'的张量'Placeholder_2：0'提供形状（256，0）的值

Question

#Import TsnsorFlow
import tensorflow as tf
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

#Imort data
data = pd.read_csv("C:\\testdata1004.csv")

#Drop data variable
data = data.drop(['DATE'],1)

#Dimensions of dataset
n = data.shape[0]
p = data.shape[1]

#Make data a numpy array
data = data.values


#Training and test data
train_start = 0
train_end = int(np.floor(0.8*n))
test_start = train_end
test_end = n
data_train = data[np.arange(train_start, train_end), :]
data_test = data[np.arange(test_start, test_end), :]

#Scale data
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
scaler.fit(data_train)
data_train = scaler.transform(data_train)
data_test = scaler.transform(data_test)

#Build X and Y
X_train = data_train[:, 1:]
y_train = data_train[:, 0]
X_test = data_test[:, 1:]
y_test = data_test[:, 0]


#Define a and b as placeholders
a = tf.placeholder(dtype=tf.int8)
b = tf.placeholder(dtype=tf.int8)

#Define a and b as placeholders
c = tf.add(a,b)

#Initialize the graph
graph = tf.Session()

#Run the graph
graph.run(c, feed_dict={a: 5, b: 4})

#Model architecture parameters
n_stocks = 500
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, n_stocks])
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 = 256

for e in range(epochs):

    #suffle 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)
​

我实现了张量流代码，但是它不起作用并且出现错误 这段代码只是一个例子，我有。我不知道我得到此错误的原因以及解决方法。你能告诉我如何解决吗？谢谢 我实现了tensorflow代码，但是它不起作用并且出现错误 这段代码只是一个例子，我有。我不知道我得到此错误的原因以及解决方法。你能告诉我如何解决吗？谢谢

Answer 1

无代表批量大小，但是对于输入到y中的数据长度，您需要另一个维度。尝试更改：

Y = tf.placeholder(dtype=tf.float32, shape=[None,1])