该实验是在Windows 10 Pro Intel（R）Core（i）i5-4590 CPU @ 3.3 GHz上进行的，基于Anaconda平台和Spyder Python 3.7.150，它是通过Python语言和Python进行编程的库功能。

我收到错误消息：

文件“ C：/Users/HSIPL/Desktop/DNN.py”，第244行，在     if（pred_img [0]

ValueError：具有多个元素的数组的真值不明确。使用a.any（）或a.all（）

如何解决此问题？

# Importing libraries
from matplotlib import pyplot as plt
from tensorflow.keras.preprocessing.image import array_to_img,img_to_array,load_img
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.image as mpimg
import numpy as np
import os

# Preparing Dataset
# Setting names of the directies for both sets
base_dir = 'data'
seta ='One'
setb ='Two'

# Each of the sets has three sub directories train,validation and test
train_dir = os.path.join(base_dir,'train')
validation_dir = os.path.join(base_dir,'validation')
test_dir = os.path.join(base_dir,'test')

def prepare_data(base_dir,seta,setb):
# Take the directory names for the base directory and both the sets 
# Returns the paths for train,validation for each of the sets
    seta_train_dir = os.path.join(train_dir,seta)
    setb_train_dir = os.path.join(train_dir,setb)

    seta_valid_dir = os.path.join(validation_dir,seta)
    setb_valid_dir = os.path.join(validation_dir,setb)

    seta_train_fnames = os.listdir(seta_train_dir)
    setb_train_fnames = os.listdir(setb_train_dir)

    return seta_train_dir,setb_train_dir,seta_valid_dir,setb_valid_dir,seta_train_fnames,setb_train_fnames

seta_train_dir,setb_train_fnames = prepare_data(base_dir,setb)

seta_test_dir = os.path.join(test_dir,seta)
setb_test_dir = os.path.join(test_dir,setb)
test_fnames_seta = os.listdir(seta_test_dir)
test_fnames_setb = os.listdir(setb_test_dir)

datagen = ImageDataGenerator( 
          height_shift_range = 0.2,width_shift_range = 0.2,rotation_range = 40,shear_range = 0.2,zoom_range = 0.2,horizontal_flip = True,fill_mode = 'nearest')

img_path = os.path.join(seta_train_dir,seta_train_fnames[3])
img = load_img(img_path,target_size = (150,150))
x = img_to_array(img)
x = x.reshape((1,) + x.shape)

i = 0
for batch in datagen.flow(x,batch_size = 1):
    plt.figure(i)
    imgplot = plt.imshow(array_to_img(batch[0]))
    i += 1
    if i % 5 == 0:
        break

# Convolutional Neural Network Model
# Import TensorFlow libraries
from tensorflow.keras import layers
from tensorflow.keras import Model       

img_input = layers.Input(shape = (150,150,3))        
x = layers.flatten()( img_input )
x = layers.Dense(512,activation = 'relu')(x)
x = layers.Dropout(0.2)(x)
x = layers.Dense(512,activation = 'relu')(x)
x = layers.Dropout(0.2)(x)
x = layers.Dense(256,activation = 'relu')(x)
x = layers.Dropout(0.2)(x)
output = layers.Dense(15,activation = 'softmax')(x)

model = Model(img_input,output)

model.summary()

import tensorflow as tf
# Using binary_crossentropy as the loss function and
# Adam Optimizer as the optimizing function when training
model.compile(loss = 'sparse_categorical_crossentropy',optimizer = tf.optimizers.Adam(learning_rate = 0.0005),metrics = ['acc'])
from tensorflow.keras.preprocessing.image import ImageDataGenerator            

# All images will be rescaled by 1./255
train_datagen = ImageDataGenerator(rescale = 1./255)
test_datagen = ImageDataGenerator(rescale = 1./255)

# Flow training images in batches of 20 using train_datagen generator
train_generator = train_datagen.flow_from_directory(
                  train_dir,150),batch_size = 20,class_mode = 'binary')

validation_generator = test_datagen.flow_from_directory(
                       validation_dir,class_mode = 'binary')

# 4x4 grid
ncols = 5
nrows = 5

pic_index = 0

# Set up matpotlib fig and size it to fit 5x5 pics
fig = plt.gcf()
fig.set_size_inches(ncols * 5,nrows * 5)

pic_index += 10
next_seta_pix = [os.path.join(seta_train_dir,fname)
                 for fname in seta_train_fnames[pic_index - 10:pic_index]]
next_setb_pix = [os.path.join(setb_train_dir,fname)
                 for fname in setb_train_fnames[pic_index - 10:pic_index]]

for i,img_path in enumerate(next_seta_pix + next_setb_pix):
# Set up subplot; subplot indices start at 1
    sp = plt.subplot(nrows,ncols,i + 1)
    sp.axis('Off')

    img = mpimg.imread(img_path)
    plt.imshow(img)

plt.show()

# Train the model
mymodel = model.fit_generator(
          train_generator,steps_per_epoch = 10,epochs = 80,validation_data = validation_generator,validation_steps = 7,verbose = 2)

import random
from tensorflow.keras.preprocessing.image import img_to_array,load_img

successive_outputs = [layer.output for layer in model.layers[1:]]
visualization_model = Model(img_input,successive_outputs)

a_img_files = [os.path.join(seta_train_dir,f) for f in seta_train_fnames]
b_img_files = [os.path.join(setb_train_dir,f) for f in setb_train_fnames]
img_path = random.choice(a_img_files + b_img_files)

img = load_img(img_path,) + x.shape)

x /= 255

successive_feature_maps = visualization_model.predict(x)

layer_names = [layer.name for layer in model.layers]

for layer_name,feature_map in zip(layer_names,successive_feature_maps):
    if len(feature_map.shape) == 4:
# Just do this for the conv/maxpool layers
        n_features = feature_map.shape[-1]
# The feature map has shape(1,size,n_features)
        size = feature_map.shape[1]
# Will tile images in this matrix
        display_grid = np.zeros((size,size * n_features))
        for i in range(n_features):
# Postprocess the feature           
            x = feature_map[0,:,i]
            x -= x.mean()

            x *= 64
            x += 128
            x = np.clip(x,255).astype('float32')
# Will tile each filter into this big horizontal grid
            display_grid[:,i * size : (i + 1) * size] = x 

# accuracy results for each training and validation epoch
acc = mymodel.history['acc']
val_acc = mymodel.history['val_acc']

# Loss Results for each training and validation epoch
loss = mymodel.history['loss']
val_loss = mymodel.history['val_loss']

epochs = range(len(acc))

# Plot accuracy for each training and validation epoch
plt.plot(epochs,acc)
plt.plot(epochs,val_acc)
plt.title('Training and validation accuracy')

plt.figure()

# Plot loss for each training and validation epoch
plt.plot(epochs,loss)
plt.plot(epochs,val_loss)
plt.title('Training and validation loss')

plt.figure()

# Testing model on a random train image from set a

train_img = random.choice(seta_train_fnames)
train_image_path = os.path.join(seta_train_dir,train_img)
train_img = load_img(train_image_path,150))
plt.imshow(train_img)
train_img = (np.expand_dims(train_img,0))
train_img = tf.cast(train_img,tf.float32)
print(train_img.shape)

model.predict(train_img)

# Testing model on a random train image from set b

train_img = random.choice(setb_train_fnames)
train_image_path = os.path.join(setb_train_dir,tf.float32)
print(train_img.shape)

model.predict(train_img)

# Testing a random image from the test set a 

cal_o = 0
cal_t = 0
cal_unconclusive = 0
alist = []
for fname in test_fnames_seta:
    if fname.startswith('.'):
        continue
    file_path = os.path.join(seta_test_dir,fname)
    load_file = load_img(file_path,150))
    load_file = (np.expand_dims(load_file,0))
    load_file = tf.cast(load_file,tf.float32)
    pred_img = model.predict(load_file)
    if(pred_img[0]<0.5):
        cal_o+=1
    elif(pred_img[0]>0.5):
        cal_t+=1
    else:
        print(pred_img[0],"\n")
        cal_unconclusive+=1
        alist.append(file_path)
print(alist)

print("Identified as:")
print("One:",cal_o)
print("Two:",cal_t)
print( "Inconclusive:",cal_unconclusive)
print( "Percentage:",(cal_o/(cal_o + cal_t + cal_unconclusive)) * 100)
a =  (cal_o/(cal_o + cal_t + cal_unconclusive)) * 100

# Testing a random image from the test set b

cal_o = 0
cal_t = 0
cal_unconclusive = 0
alist = []
for fname in test_fnames_setb:
    if fname.startswith('.'):
        continue
    file_path = os.path.join(setb_test_dir,(cal_t/(cal_o + cal_t + cal_unconclusive)) * 100)
b =  (cal_t/(cal_o + cal_t + cal_unconclusive)) * 100             

avg = (a+b)/2
print("\nAverage Percentage:",avg)

请帮忙，谢谢---