Я планирую сделать классификацию 2 классов с керасами с CNN. Вот код
import random
import pandas as pd
from pandas import Series,DataFrame
import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.datasets import fashion_mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout,Activation ,Flatten, Dropout,
Conv2D, MaxPooling2D, Reshape
from keras.optimizers import RMSprop
from keras.optimizers import Adam
df_list = []
for i in range(4):
cl_data_set = pd.read_csv("./csv/cl_" + str(i) +".csv",sep=",",header=0)
cl_x = DataFrame(cl_data_set.drop("POINT",axis=1))
df_list.append(cl_x)
op_data_set = pd.read_csv("./csv/op_" + str(i) +".csv",sep=",",header=0)
op_x = DataFrame(op_data_set.drop("POINT",axis=1))
df_list.append(op_x)
close_1 = np.array(df_list[0])
open_1 = np.array(df_list[1])
close_2 = np.array(df_list[2])
open_2 = np.array(df_list[3])
close_3 = np.array(df_list[4])
open_3 = np.array(df_list[5])
close_4 = np.array(df_list[6])
open_4 = np.array(df_list[7])
train_dataset = []
test_dataset = []
for i in range(0,13000,100):
if i >= 10400:
test_dataset.append([close_1[i:i+100,:],1])
test_dataset.append([close_2[i:i+100,:],1])
test_dataset.append([open_1[i:i+100,:],0])
test_dataset.append([open_2[i:i+100,:],0])
else:
train_dataset.append([close_1[i:i+100,:],1])
train_dataset.append([close_2[i:i+100,:],1])
train_dataset.append([open_1[i:i+100,:],0])
train_dataset.append([open_2[i:i+100,:],0])
random.shuffle(train_dataset)
random.shuffle(test_dataset)
x_train_list = []
y_train_list = []
x_test_list = []
y_test_list = []
for i in range(416):
x_train_list.append(train_dataset[i][0])
y_train_list.append(train_dataset[i][1])
for i in range(104):
x_test_list.append(test_dataset[i][0])
y_test_list.append(test_dataset[i][1])
x_train = np.array(x_train_list)
x_test = np.array(x_test_list)
y_train = np.array(y_train_list)
y_test = np.array(y_test_list)
model = Sequential()
x_train = np.reshape(x_train,(-1,416,100,4))
model.add(Conv2D(32, kernel_size=(3,3), activation='relu', input_shape=(416,100,4)))
model.add(MaxPooling2D(pool_size=(1,2)))
model.add(Dropout(0.4))
model.add(Flatten())
model.add(Dense(128, activation='sigmoid'))
model.add(Dropout(0.4))
model.add(Dense(1, activation='sigmoid'))
model.summary()
print("\n")
model.compile(loss='binary_crossentropy',optimizer="Adam",metrics=['accuracy'])
history = model.fit(x_train, y_train,batch_size=200,epochs=2000,verbose=1,validation_data=(x_test, y_test))
score = model.evaluate(x_test,y_test,verbose=1)
print("\n")
print("Test loss:",score[0])
print("Test accuracy:",score[1])
Далее я выложу полный список ошибок
Использование TensorFlow backend.
x_train.shape
(416, 100, 4)
x_test.shape
(104, 100, 4)
y_train.shape
(416,)
y_test.shape
(104,)
Traceback (most recent call last):
File "CNN.py", line 121, in <module>
history = model.fit(x_train, y_train,batch_size=200,epochs=2000,verbose=1,validation_data=(x_test, y_test))
File "C:\Anaconda3\envs\tensorflow\lib\site-packages\keras\engine\training.py", line 950, in fit
batch_size=batch_size)
File "C:\Anaconda3\envs\tensorflow\lib\site-packages\keras\engine\training.py", line 802, in _standardize_user_data
check_array_length_consistency(x, y, sample_weights)
File "C:\Anaconda3\envs\tensorflow\lib\site-packages\keras\engine\training_utils.py", line 236, in check_array_length_consistency
'and ' + str(list(set_y)[0]) + ' target samples.')
ValueError: Input arrays should have the same number of samples as target arrays. Found 1 input samples and 416 target samples.
Общее количество образцов составляет 520 штук, разделенных на поезда: тест = 8: 2,
х имеет 4-канальные данные, разделенные на 100 точек, а у имеет метку (0 или 1).
Пожалуйста, сотрудничайте с решением проблемы и необходимой коррекцией