0
我想預測數據的新實例的使用tensorflow前饋DNN圖分類評估新的數據實例時。ValueError異常試圖tensorflow
的代碼是:
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)
的錯誤是:
ValueError: Cannot feed value of shape (7,) for Tensor 'Placeholder:0', which has shape '(?, 7)'
在這條線
classification = y.eval(feed_dict={x: [51.0,10.0,71.0,65.0,5.0,70.0,30.06]})
我不知道從哪裏開始,因爲我不想讓改變我的佔位符中的值,因爲我認爲他們是他們需要的地方。任何幫助表示讚賞。謝謝!
謝謝。您的更正解決了錯誤,但正在創建一個新的。 InvalidArgumentError(請參閱上面的回溯):您必須爲dtype float提供佔位符張量'Placeholder_1'的值 \t [[節點:Placeholder_1 =佔位符[dtype = DT_FLOAT,shape = [],_device =「/ job:localhost /副本:0 /任務:0/CPU:0" ]()]] 我應該紀念這個作爲解決然後開始一個新的線程,或者與新的問題刪除嗎? –
沒有probs。這是一個不同的問題,但很快,既然你(1)tf.argmax(預測,1),tf.argmax(Y)中定義Y = tf.placeholder( '浮動'),並正確= tf.equal您train_neural_network功能tensorflow期待你給你的價值。我想你的意思是X爲輸入和Y是神經網絡的輸出。如果是這種情況,那麼y移除作爲佔位符和糾正你得到錯誤,你應該沒問題。評估前,您可能需要y = neural_network_model()。希望這會有所幫助,否則會產生另一個問題,您可能會獲得更多的細節。 –
太好了,謝謝 –