我在构建训练模型时遇到问题。它返回的"int"对象是不可调用的。
这是我的代码：
from __future__ import print_function
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.utils import np_utils
from keras import backend as K
import pandas as pd
import numpy as np
# input image dimensions
img_rows, img_cols = 7, 7
# fix random seed for reproducibility
seed = 7
np.random.seed(seed)
x_train = pd.read_csv('palm_3x3_test.csv')
x_train.drop(['class'],axis=1,inplace=True)
x_train = x_train.as_matrix().reshape(-1, 7, 7)
y_train = pd.read_csv('palm_3x3_test.csv')
y_train = y_train[['class']]
x_test = pd.read_csv('palm_3x3_data.csv')
x_test.drop(['class'],axis=1,inplace=True)
x_test = x_test.as_matrix().reshape(-1, 7, 7)
y_test = pd.read_csv('palm_3x3_data.csv')
y_test = y_test[['class']]
# reshape to be [samples][pixels][width][height]
x_train_final = x_train.reshape(x_train.shape[0], 7, 7,1).astype('float32')
x_test_final = x_test.reshape(x_test.shape[0], 7, 7,1).astype('float32')
# normalize inputs from 0-255 to 0-1
x_train_final = x_train_final / 255
x_test_final = x_test_final / 255
# one hot encode outputs
y_train = np_utils.to_categorical(y_train)
y_test = np_utils.to_categorical(y_test)
num_classes = y_test.shape[1]
input_shape = (img_rows,img_cols , 1)
def baseline_model():
# create model
model = Sequential()
model.add(Conv2D(30 (5,5), border_mode='valid', input_shape=(1,(7,7)), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(15 (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(50, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))
# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
# build the model
model = baseline_model()
# Fit the model
model.fit(x_train_final,y_train_final, validation_data=(x_test,y_test), nb_epoch=10, batch_size=200,verbose=2)
# Final evaluation of the model
scores = model.evaluate(x_test,y_test, verbose=0)
print("CNN Error: %.2f%%" % (100-scores[1]*100))
这是错误：
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-42-7f55d9765a8e> in <module>()
76
77 # build the model
---> 78 model = baseline_model()
79 # Fit the model
80 model.fit(x_train_final,y_train_final, validation_data=(x_test,y_test), nb_epoch=10, batch_size=200,verbose=2)
<ipython-input-42-7f55d9765a8e> in baseline_model()
61
62 model = Sequential()
---> 63 model.add(Conv2D(30 (5,5), border_mode='valid', input_shape=(1,(7,7)), activation='relu'))
64 model.add(MaxPooling2D(pool_size=(2, 2)))
65 model.add(Conv2D(15 (3, 3), activation='relu'))
TypeError: 'int' object is not callable
我尝试搜索相关错误，大多数答案都是关于变量命名冲突函数名称的说法。此错误的原因是因为我对变量的命名吗？
30后缺少逗号
model.add(Conv2D(30 (5,5), border_mode='valid', input_shape=(1,(7,7)), activation='relu'))
这就是为什么它试图使用参数调用305,5.