Input and target (teacher) sequences do not match on keras CNN

import random
import pandas aspd
from pandas import Series, DataFrame

import numpy as np
import matplotlib.pyplot asplt

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.validate(x_test,y_test,verbose=1)
print("\n")
print("Test loss:", score[0])
print("Test acuity:", score[1])

Using 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, infit
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, 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.