t学习模型.预测无法馈送形状（1，1，17）的值

Question

我对Tensorflow和tflearn还很陌生 到目前为止，我已经研究了一些教程，并尝试将tflearn泰坦尼克号示例应用于动物园动物数据集（http://archive.ics.uci.edu/ml/datasets/Zoo）。训练效果很好，但当我尝试使用模型.预测对于我输入的数据，它给出了以下错误

无法为张量“InputData/X:0”输入形状（1，1，17）的值，该张量具有shape'（？，16）“

下面是python代码

from __future__ import print_function

import numpy as np
import tflearn

# Load CSV file, indicate that the first column represents labels
from tflearn.data_utils import load_csv
data, labels = load_csv('zoo.csv', target_column=-1,
                        categorical_labels=True, n_classes=8)


# Preprocessing function
def preprocess(data, columns_to_ignore):
    # Sort by descending id and delete columns
    for id in sorted(columns_to_ignore, reverse=True):
        [r.pop(id) for r in data]
    return np.array(data, dtype=np.float32)

# Ignore 'name' and 'ticket' columns (id 1 & 6 of data array)
to_ignore=[0]

# Preprocess data
data = preprocess(data, to_ignore)

# Build neural network
net = tflearn.input_data(shape=[None,16])
net = tflearn.fully_connected(net, 128)
net = tflearn.dropout(net, 1)
net = tflearn.fully_connected(net, 128)
net = tflearn.dropout(net, 1)
net = tflearn.fully_connected(net, 8, activation='softmax')
net = tflearn.regression(net)


# Define model
model = tflearn.DNN(net)
# Start training (apply gradient descent algorithm)
model.fit(data, labels, n_epoch=1, validation_set=0.1, shuffle=True, batch_size=17, show_metric=True)

ant = ['ant', 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 8, 0, 0, 0]
# Preprocess data
ant = preprocess([ant], to_ignore)
# Predict surviving chances (class 1 results)
pred = model.predict([ant])
print("Ant is:", pred[0])

我试过用整形，效果不太好。我在搜索中发现的类似问题都是在训练阶段出现的，而不是预测。在

Answer 1

结果我对数据集中的列数看得不够仔细。。。 如果其他人遇到类似的问题，或者使用这个例子来练习机器学习，这里是可以工作的代码。在

from __future__ import print_function

import numpy as np
import tflearn

# Load CSV file, indicate that the first column represents labels
from tflearn.data_utils import load_csv
data, labels = load_csv('zoo.csv', target_column=-1,
                        categorical_labels=True, n_classes=8)


# Preprocessing function
def preprocess(data, columns_to_ignore):
    # Sort by descending id and delete columns
    for id in sorted(columns_to_ignore, reverse=True):
        [r.pop(id) for r in data]
    return np.array(data, dtype=np.float32)

# Ignore 'name' and 'ticket' columns (id 1 & 6 of data array)
to_ignore=[0]

# Preprocess data
data = preprocess(data, to_ignore)

# Build neural network
net = tflearn.input_data(shape=[None,16])
net = tflearn.fully_connected(net, 128)
net = tflearn.dropout(net, 1)
net = tflearn.fully_connected(net, 128)
net = tflearn.dropout(net, 1)
net = tflearn.fully_connected(net, 8, activation='softmax')
net = tflearn.regression(net)


# Define model
model = tflearn.DNN(net)
# Start training (apply gradient descent algorithm)
model.fit(data, labels, n_epoch=30, validation_set=0.1, shuffle=True, batch_size=20, show_metric=True)

ant = [0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 8, 0, 0, 0]
# Preprocess data
# ant = preprocess([ant], to_ignore)
# ant = np.reshape(ant, (1,16))
# Predict surviving chances (class 1 results)
pred = model.predict_label([ant])
print("Ant is:", pred[0])