評估張量流中的新數據時出現InvalidArgumentError錯誤

Question

我試圖使用張量流中的前饋DNN圖來預測新數據實例的分類。

這是此螺紋，這是解決ValueError when trying to evaluate a new data instance in tensorflow

該代碼的延續是：

import tensorflow as tf 
import pandas as pd 

dataframe = pd.read_csv("jfkspxstrain.csv") # Let's have Pandas load our dataset as a dataframe 
dataframe = dataframe.drop(["Field6", "Field9", "rowid"], axis=1) # Remove columns we don't care about 
dataframe.loc[:, ("y2")] = dataframe["y1"] == 0   # y2 is the negation of y1 
dataframe.loc[:, ("y2")] = dataframe["y2"].astype(int) # Turn TRUE/FALSE values into 1/0 
trainX = dataframe.loc[:, ['Field2', 'Field3', 'Field4', 'Field5', 'Field7', 'Field8', 'Field10']].as_matrix() 
trainY = dataframe.loc[:, ["y1", 'y2']].as_matrix() 

dataframe = pd.read_csv("jfkspxstest.csv") # Let's have Pandas load our dataset as a dataframe 
dataframe = dataframe.drop(["Field6", "Field9", "rowid"], axis=1) # Remove columns we don't care about 
dataframe.loc[:, ("y2")] = dataframe["y1"] == 0   # y2 is the negation of y1 
dataframe.loc[:, ("y2")] = dataframe["y2"].astype(int) # Turn TRUE/FALSE values into 1/0 
testX = dataframe.loc[:, ['Field2', 'Field3', 'Field4', 'Field5', 'Field7', 'Field8', 'Field10']].as_matrix() 
testY = dataframe.loc[:, ["y1", 'y2']].as_matrix() 

n_nodes_hl1 = 10 
n_nodes_hl2 = 10 
n_nodes_hl3 = 10 

n_classes = 2 
batch_size = 1 

x = tf.placeholder('float',[None, 7]) 
y = tf.placeholder('float') 

def neural_network_model(data): 
    hidden_1_layer = {'weights':tf.Variable(tf.random_normal([7, n_nodes_hl1])), 
         'biases':tf.Variable(tf.random_normal([n_nodes_hl1]))} 

    hidden_2_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])), 
         'biases':tf.Variable(tf.random_normal([n_nodes_hl2]))} 

    hidden_3_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl2, n_nodes_hl3])), 
         'biases':tf.Variable(tf.random_normal([n_nodes_hl3]))} 

    output_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl1, n_classes])), 
         'biases':tf.Variable(tf.random_normal([n_classes]))} 
    l1 = tf.add(tf.matmul(data, hidden_1_layer['weights']), hidden_1_layer['biases']) 
    l1 = tf.nn.relu(l1) 

    l2 = tf.add(tf.matmul(l1, hidden_2_layer['weights']), hidden_2_layer['biases']) 
    l2 = tf.nn.relu(l2) 

    l3 = tf.add(tf.matmul(l2, hidden_3_layer['weights']), hidden_3_layer['biases']) 
    l3 = tf.nn.relu(l3) 

    output = tf.matmul(l3, output_layer['weights']) + output_layer['biases']  
    return output 

def train_neural_network(x): 
    prediction = neural_network_model(x) 
    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(prediction,y)) 
    optimizer = tf.train.AdamOptimizer().minimize(cost) 

    hm_epochs = 5 

    with tf.Session() as sess: 
     sess.run(tf.initialize_all_variables()) 

     for epoch in range(hm_epochs): 
      epoch_loss = .1 
      for _ in range(399): 
       _, c = sess.run([optimizer, cost], feed_dict = {x: trainX, y: trainY}) 
       epoch_loss += c 
      print('Epoch', epoch, 'completed out of', hm_epochs, 'loss:', epoch_loss) 

     correct = tf.equal(tf.argmax(prediction,1), tf.argmax(y,1)) 
     accuracy = tf.reduce_mean(tf.cast(correct, 'float')) 
     print('Accuracy:',accuracy.eval({x: testX, y: testY})) 
     classification = y.eval(feed_dict={x: [[51.0,10.0,71.0,65.0,5.0,70.0,30.06]]}) 
     print (classification) 
train_neural_network(x) 

錯誤是：

InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'Placeholder_1' with dtype float 
    [[Node: Placeholder_1 = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]()]] 

在這條線

classification = y.eval(feed_dict={x: [[51.0,10.0,71.0,65.0,5.0,70.0,30.06]]}) 

代碼和數據在這裏：https://github.com/jhsmith12345/tensorflow

我看到，這是要求浮動值，我認爲我已經餵它。任何幫助表示讚賞！

Answer 1

因此，假設y是您的目標數據，在訓練期間，您需要爲目標數據提供一個值，以便可以計算出錯誤。該生產線是classification = y.eval獲得預測，所以應該沒有要求提供培訓資料 - 所以應該classification = prediction.eval ...

def train_neural_network(x): 
    prediction = neural_network_model(x) 
    cost =tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(prediction,y)) 
optimizer = tf.train.AdamOptimizer().minimize(cost) 

    hm_epochs = 5 

    with tf.Session() as sess: 
    sess.run(tf.initialize_all_variables()) 

    for epoch in range(hm_epochs): 
     epoch_loss = .1 
     for _ in range(399): 
      _, c = sess.run([optimizer, cost], feed_dict = {x: trainX, y: trainY}) 
      epoch_loss += c 
     print('Epoch', epoch, 'completed out of', hm_epochs, 'loss:', epoch_loss) 

    correct = tf.equal(tf.argmax(prediction,1), tf.argmax(y,1)) 
    accuracy = tf.reduce_mean(tf.cast(correct, 'float')) 
    print('Accuracy:',accuracy.eval({x: testX, y: testY})) 
    classification = prediction.eval(feed_dict={x: [[51.0,10.0,71.0,65.0,5.0,70.0,30.06]]}) 
    print (classification) 

train_neural_network(x)