层sequential_10的输入0与层不兼容：：预期min_ndim=4，发现ndim=2

Question

在重塑 xtraindata 和 xtest 数据之前，我得到了错误： “层序 _10 的输入 0 与层不兼容：：预期 min_ndim=4，发现 ndim=2。”。按顺序将 xtraindata 和 xtestdata 重塑为 (1400,24,24,1) 和 (600,24,24,1)。然后我得到这样的错误： “不兼容的形状：[32,1] 与 [32,6,6,1] [[节点 mean_squared_error/SquaredDifference（定义在 C:\Users\User\Documents\car_person.py:188）]] [Op:__inference_test_function_7945]

函数调用栈： 测试功能” 我无法让评估功能在创建的模型上工作。我应该怎么做才能使测试数据与模型兼容？

import numpy as np
import matplotlib.pyplot as plt
import os
import time
import cv2
import pandas as pd
import tensorflow as tf
import itertools as it
from sklearn.decomposition import PCA
from sklearn.model_selection import train_test_split
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
  try:
    tf.config.experimental.set_virtual_device_configuration(gpus[0], [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=4096)])
  except RuntimeError as e:
    print(e)


#gpu_options=K.tf.GPUOptions(per_process_gpu_memory_fraction=0.35)

path = "C:/Users/User/Desktop/tunel_data"
training_data=[]

def create_training_data(training_data, path):
    categories = ["tunel_data_other", "tunel_data_car"]
    for category in categories:
        path=os.path.join(path, category)
        for img in os.listdir(path):
            print(img)
            if category=="tunel_data_other":
                class_num= 0
                #image=Image.open(img)
                #new_image = image.resize((50, 50))
                #new_image.save('car'+img.index())
                #try:
                image_array = cv2.imread(os.path.join(path, img), cv2.IMREAD_GRAYSCALE)/255
                new_array = cv2.resize(image_array, (24, 24))
                print(new_array.shape)
                training_data.append([new_array, class_num])
                #except:
                    #pass
            elif category=="tunel_data_car":
                class_num = 1
                #image=Image.open(img)
                #new_image = image.resize((50, 50))
                #new_image.save('person'+img.index())
                #try:
                image_array = cv2.imread(os.path.join(path, img), cv2.IMREAD_GRAYSCALE)/255
                new_array = cv2.resize(image_array, (24, 24))
                print(new_array.shape)
                training_data.append([new_array, class_num])
                #except:
                    #pass
        path = "C:/Users/User/Desktop/tunel_data"
    return training_data

create_training_data(training_data, path)

x=[]
y=[]

for i in range(len(training_data)):
    x.append(training_data[i][0])
    y.append(training_data[i][1])
#print(x)
#print(y)
     
x = np.array(x).reshape(2000, 576)
"""
principle_features = PCA(n_components=250)
feature = principle_features.fit_transform(x)
"""
feature = x
label = y

feature_df = pd.DataFrame(feature)

#df = DataFrame (People_List,columns=['First_Name','Last_Name','Age'])

label_df = pd.DataFrame(label)


data = pd.concat([feature_df, label_df], axis=1).to_csv('complete.csv')


data = pd.read_csv("complete.csv")

data = data.sample(frac=1).reset_index(drop=True)

print(data)

x_test, x_train, y_test, y_train = train_test_split(x, y, test_size=0.7, random_state=65)
xtraindata=pd.DataFrame(data=x_train[:,:])
xtestdata=pd.DataFrame(data=x_test[:,:])
print(xtraindata)

ytraindata=pd.DataFrame(data=y_train[:])
ytestdata=pd.DataFrame(data=y_test[:])
print(ytraindata)

xtraindata = np.asarray(xtraindata)
ytraindata = np.asarray(ytraindata)
xtestdata = np.asarray(xtestdata)
ytestdata = np.asarray(ytestdata)
x=np.asarray(x)
y=np.asarray(y)


xtraindata = xtraindata.reshape(1400,24,24,1)
xtestdata = xtestdata.reshape(600,24,24,1)

activation = ["tanh", "relu", "sigmoid", "softmax"]
input_size1 = range(10)
input_size2 = range(10)
k_scores = []
in_size = []

possible = list(it.permutations(activation, 4))

for c in possible:
    for i in input_size1:
        for a in input_size2:
            model = tf.keras.Sequential([tf.keras.layers.Conv2D(256, kernel_size=(3,3), padding='same', activation='relu'),
                                         tf.keras.layers.MaxPooling2D(pool_size=(2,2)),
                                         tf.keras.layers.Conv2D(512, kernel_size=(3,3), padding='same', activation='relu'),
                                         tf.keras.layers.MaxPooling2D(pool_size=(2,2)),
                                         tf.keras.layers.Dense(250, activation=c[0]),
                                         tf.keras.layers.Dense(i, activation=c[1]),
                                         tf.keras.layers.Dense(a, activation=c[2]),
                                         tf.keras.layers.Dense(1, activation=c[3])])
            model.compile(optimizer='sgd', loss='mse')
            val_loss = model.evaluate(xtestdata, ytestdata, verbose=1)
            k_scores.append(val_loss)
            in_size.append([i,a])
            
print(k_scores)
print("Best activation functions for each layer:", possible[(k_scores.index((min(k_scores)))) % len(possible)],
      "/n Best input sizes:", "840", in_size[k_scores.index((min(k_scores)))][0], in_size[k_scores.index((min(k_scores)))][1], "1")

model = tf.keras.Sequential()
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(250, activation=possible[(k_scores.index((min(k_scores)))) % len(possible)][0]))
model.add(tf.keras.layers.Dense(in_size[k_scores.index((min(k_scores)))][0], activation=possible[(k_scores.index((min(k_scores)))) % len(possible)][1]))
model.add(tf.keras.layers.Dense(in_size[k_scores.index((min(k_scores)))][1], activation=possible[(k_scores.index((min(k_scores)))) % len(possible)][2]))
model.add(tf.keras.layers.Dense(1, activation=possible[(k_scores.index((min(k_scores)))) % len(possible)][3]))
model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy", "mse"])
model.fit(x, y, batch_size=16, epochs=5)
predictions = model.predict([x_test])
print(predictions)
print(predictions.shape)

Answer 1

输出层大小不同。你想要大小(32, 1)，但模型的输出是(32, 6, 6, 1)

在MaxPooling2D 和Dense() 之间插入Flatten()，也许这项工作很好。

这里是提示。 .evaluate 方法仅适用于经过训练的模型。你应该先使用.fit。