python tensorflow - 在 mac 和 ubuntu 上运行相同的代码 - ubuntu 抛出错误

Question

我在 mac 和 ubuntu 上运行相同的神经网络模型。 NN 在一个循环中运行 3 次并输出预测和准确性。代码如下：

import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
import pandas as pd
import matplotlib
from matplotlib import style
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from keras.layers.normalization import BatchNormalization
from keras.optimizers import RMSprop
import numpy as np
from functools import reduce
import tensorflow as tf
from keras import backend as K
import os

#%matplotlib inline
matplotlib.style.use('ggplot')

def neural_net(set_val, epoch_val, batch_val, label_val):
    sess = tf.Session()
    K.set_session(sess)
    server = tf.train.Server.create_local_server()
    sess = tf.Session(server.target)
    init_op = tf.global_variables_initializer()
    sess.run(init_op)

    def neural_net_1(label1, pred_1):

        data_num_train = pd.read_csv(str(set_val) + 'final_train_set_all_2.csv')
        data_num_test = pd.read_csv(str(set_val) + 'final_test_set_all_2.csv')

        scaler = MinMaxScaler(feature_range=(0, 1))

        # Train Data
        features_train = data_num_train.drop([label1], axis=1, errors='ignore')
        features_train = pd.DataFrame(scaler.fit_transform(features_train))
        scale_num_data_train = pd.concat([data_num_train[label1], features_train], axis=1)
        dtrain_train, dtest_train = train_test_split(scale_num_data_train, test_size=0.01, random_state=575)
        X_train = dtrain_train.drop([label1], axis=1, errors='ignore')
        y_train = dtrain_train[label1]

        # Test Data
        features_test = data_num_test.drop([label1], axis=1, errors='ignore')
        features_test = pd.DataFrame(scaler.fit_transform(features_test))
        scale_num_data_test = pd.concat([data_num_test[label1], features_test], axis=1)
        dtrain_test, dtest_test = train_test_split(scale_num_data_test, test_size=0.99, random_state=575)
        X_test = dtest_test.drop([label1], axis=1, errors='ignore')
        y_test = dtest_test[label1]

        # initiate model
        model = Sequential()

        # Input Layer 1
        model.add(Dense(256, input_dim=1970, init='uniform'))
        model.add(BatchNormalization())
        model.add(Activation('tanh'))
        model.add(Dropout(0.2))

        # Hidden Layer 2
        model.add(Dense(256, init='uniform'))
        model.add(BatchNormalization())
        model.add(Activation('tanh'))
        model.add(Dropout(0.2))

        # Hidden Layer 3
        model.add(Dense(256, init='uniform'))
        model.add(BatchNormalization())
        model.add(Activation('tanh'))
        model.add(Dropout(0.2))

        # Hidden Layer 4
        model.add(Dense(256, init='uniform'))
        model.add(BatchNormalization())
        model.add(Activation('tanh'))

        # Output Layer 5
        model.add(Dense(10, init='uniform'))
        model.add(BatchNormalization())
        model.add(Activation('softmax'))

        model.summary()
        rms_prop = RMSprop(lr=0.0001, rho=0.9, epsilon=None, decay=0.0)
        model.compile(optimizer=rms_prop, loss='sparse_categorical_crossentropy', metrics=['accuracy'])
        model.fit(X_train, y_train, epochs=epoch_val, batch_size=batch_val, shuffle=True)

        scores = model.evaluate(X_test, y_test, batch_size=batch_val)
        print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))

        acc_random, accuracy_dict = ("%s: %.2f%%" % (model.metrics_names[1], scores[1] * 100)).split(" ", 1)
        accuracy_dict = accuracy_dict.replace('%', '')
        accuracy_dict = {str(pred_1): accuracy_dict}
        accuracy_df = pd.DataFrame(data=accuracy_dict, index=[0])
        accuracy_df.to_csv('mid_accuracy' + str(set_val) + '_' + label1 + '_' + str(pred_1) + '.csv')

        pred = model.predict_classes(X_test)
        print(pred)
        print(type(pred))

        # output: <class 'numpy.ndarray'>

        Xtest = scaler.inverse_transform(X_test)
        Xtest = pd.DataFrame(data=Xtest[0:, 0:], columns=Xtest[0, 0:])
        print(Xtest.head(n=20))
        print(type(Xtest))

        # output: [20 rows x 1970 columns]
        # output: <class 'pandas.core.frame.DataFrame'>

        Xtest['prediction' + str(pred_1) + str(label1)] = pred

        col_vals = pd.read_csv(str(set_val) + 'final_test_set_all_2.csv')
        col_vals = col_vals.columns.get_values()
        col_val_list = col_vals.tolist()
        col_val_list.extend(['prediction' + '_' + str(set_val) + '_' + label1 + '_' + str(epoch_val) + '_' + str(batch_val) + '_' + str(pred_1)])
        col_val_list.remove(label1)

        Xtest.columns = col_val_list
        Xtest.to_csv('mid_prediction' + '_' + str(set_val) + '_' + label1 + '_' + str(epoch_val) + '_' + str(batch_val) + '_' + str(pred_1) + '.csv')
        return None

    prediction_list2 = [1, 2, 3]
    for prediction2 in prediction_list2:
        neural_net_1(label_val, prediction2)

    sess.close()


neural_net(set_val=21, epoch_val=5,
           batch_val=24, label_val='label1')

但是，当我在 ubuntu 上运行此代码时，出现以下错误：

TypeError: Cannot convert bool to numpy.ndarray

在这一行：

Xtest['prediction' + str(pred_1) + str(label1)] = pred

此外，在这两种情况下，

pred 变量是：

<class 'numpy.ndarray'>

Xtest 变量是：

<class 'pandas.core.frame.DataFrame'>

mac 运行 python 3.6 和 tensorflow 1.6 - cpu。

ubuntu 正在运行 python 3.5 和 tensorflow 1.5 - gpu

如果有人能帮助我理解为什么错误发生在一个实例而不是另一个实例以及我如何解决它，我将不胜感激。谢谢。

Answer 1

要将列添加到熊猫数据框中，您应该这样做

col_name = 'prediction' + str(pred_1) + str(label1)
Xtest = Xtest.assign(col_name=pred)