我正在尝试使用我自己的数据运行一个程序，其中包含 4 个特征和 11599 行。 我收到此错误： ValueError：层“生成器”的输入 1 与该层不兼容：预期形状=（无，128），发现形状=（32, 20） 下面是完整的错误。
ValueError Traceback (most recent call last)
<ipython-input-31-966c8c98f44b> in <module>()
176 if __name__ == '__main__':
177 malgan = MalGAN()
--> 178 malgan.train(epochs=1000, batch_size=128)
2 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py in autograph_handler(*args, **kwargs)
ValueError: in user code:
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1801, in predict_function *
return step_function(self, iterator)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1790, in step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1783, in run_step **
outputs = model.predict_step(data)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1751, in predict_step
return self(x, training=False)
File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 67, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/usr/local/lib/python3.7/dist-packages/keras/engine/input_spec.py", line 264, in assert_input_compatibility
raise ValueError(f'Input {input_index} of layer "{layer_name}" is '
ValueError: Input 1 of layer "generator" is incompatible with the layer: expected shape=(None, 128), found shape=(32, 20)
这是我的代码：
from __future__ import print_function, division
from keras.layers import Input, Dense, Activation
from keras.layers import Maximum, Concatenate
from keras.models import Model
from tensorflow.keras.optimizers import Adam
from sklearn.ensemble import RandomForestClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
class MalGAN():
def __init__(self):
self.apifeature_dims = 128
self.z_dims = 20 #could try 20,malware
self.hide_layers = 256
self.generator_layers = [self.apifeature_dims+self.z_dims, self.hide_layers, self.apifeature_dims]
self.substitute_detector_layers = [self.apifeature_dims, self.hide_layers, 1]
self.blackbox = 'MLP'
optimizer = Adam(lr=0.001)
# Build and Train blackbox_detector
self.blackbox_detector = self.build_blackbox_detector()
# Build and compile the substitute_detector
self.substitute_detector = self.build_substitute_detector()
self.substitute_detector.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])
# Build the generator
self.generator = self.build_generator()
# The generator takes malware and noise as input and generates adversarial malware examples
example = Input(shape=(128,),name='exampe',dtype='float32')
noise = Input(shape=(128,),name='noise',dtype='float32')
input = [example, noise]
malware_examples = self.generator(input)
# For the combined model we will only train the generator
self.substitute_detector.trainable = True
# The discriminator takes generated images as input and determines validity
validity = self.substitute_detector(malware_examples)
# The combined model (stacked generator and substitute_detector)
# Trains the generator to fool the discriminator
self.combined = Model(input, validity)
self.combined.compile(loss='binary_crossentropy', optimizer=optimizer)
def build_blackbox_detector(self):
if self.blackbox is 'MLP':
blackbox_detector = MLPClassifier(hidden_layer_sizes=(50,), max_iter=10, alpha=1e-4,
solver='sgd', verbose=0, tol=1e-4, random_state=1,
learning_rate_init=.1)
return blackbox_detector
def build_generator(self):
example = Input(shape=(128,),name='exampe',dtype='float32')
noise = Input(shape=(128,),name='noise',dtype='float32')
x = Concatenate(axis=1)([example, noise])
for dim in self.generator_layers[1:]:
x = Dense(dim)(x)
x = Activation(activation='sigmoid')(x)
x = Maximum()([example, x])
generator = Model([example, noise], x, name='generator')
generator.summary()
return generator
def build_substitute_detector(self):
input = Input(shape=(128,),dtype='float32')
x = input
for dim in self.substitute_detector_layers[1:]:
x = Dense(dim)(x)
x = Activation(activation='sigmoid')(x)
substitute_detector = Model(input, x, name='substitute_detector')
substitute_detector.summary()
return substitute_detector
def load_data(self, filename):
# data = pd.read_csv("feature_vectors_system calls.csv")
data = np.load(filename)
xmal, ymal, xben, yben = data['arr_0'].reshape(-1,1), data['arr_1'].reshape(-1,1), data['arr_2'].reshape(-1,1), data['arr_3'].reshape(-1,1)
return (xmal, ymal), (xben, yben)
def train(self, epochs, batch_size=128):
# Load the dataset
(xmal, ymal), (xben, yben) = self.load_data('my_data.npz')
xtrain_mal, xtest_mal, ytrain_mal, ytest_mal = train_test_split(xmal, ymal, test_size=0.20)
xtrain_ben, xtest_ben, ytrain_ben, ytest_ben = train_test_split(xben, yben, test_size=0.20)
print("here 1")
# Train blackbox_detctor
self.blackbox_detector.fit(np.concatenate([xmal, xben]),
np.concatenate([ymal, yben]))
ytrain_ben_blackbox = self.blackbox_detector.predict(xtrain_ben)
Original_Train_TRR = self.blackbox_detector.score(xtrain_mal, ytrain_mal)
Original_Test_TRR = self.blackbox_detector.score(xtest_mal, ytest_mal)
print("here 2")
Train_TRR, Test_TRR = [], []
for epoch in range(epochs):
for step in range(1):#range(xtrain_mal.shape[0] // batch_size):
# ---------------------
# Train substitute_detector
# ---------------------
# Select a random batch of malware examples
idx = np.random.randint(0, xtrain_mal.shape[0],batch_size)
xmal_batch = xtrain_mal[idx]
noise = np.random.uniform(0, 1, (batch_size, self.z_dims))
idx = np.random.randint(0, xmal_batch.shape[0], batch_size)
xben_batch = xtrain_ben[idx]
yben_batch = ytrain_ben_blackbox[idx]
# Generate a batch of new malware examples
gen_examples = self.generator.predict([xmal_batch, noise])
print("xmal_batch is:",xmal_batch)
ymal_batch = self.blackbox_detector.predict(np.ones(gen_examples.shape)*(gen_examples > 0.5))
# Train the substitute_detector
d_loss_real = self.substitute_detector.train_on_batch(gen_examples, ymal_batch)
d_loss_fake = self.substitute_detector.train_on_batch(xben_batch, yben_batch)
d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
# ---------------------
# Train Generator
# ---------------------
idx = np.random.randint(0, xtrain_mal.shape[0], batch_size)
xmal_batch = xtrain_mal[idx]
noise = np.random.uniform(0, 1, (batch_size, self.z_dims))
# Train the generator
g_loss = self.combined.train_on_batch([xmal_batch, noise], np.zeros((batch_size, 1)))
# Compute Train TRR
noise = np.random.uniform(0, 1, (xtrain_mal.shape[0], self.z_dims))
gen_examples = self.generator.predict([xtrain_mal, noise])
TRR = self.blackbox_detector.score(np.ones(gen_examples.shape) * (gen_examples > 0.5), ytrain_mal)
Train_TRR.append(TRR)
# Compute Test TRR
noise = np.random.uniform(0, 1, (xtest_mal.shape[0], self.z_dims))
gen_examples = self.generator.predict([xtest_mal, noise])
TRR = self.blackbox_detector.score(np.ones(gen_examples.shape) * (gen_examples > 0.5), ytest_mal)
Test_TRR.append(TRR)
# Plot the progress
print("%d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100*d_loss[1], g_loss))
print('Original_Train_TRR: {0}, Adver_Train_TRR: {1}'.format(Original_Train_TRR, Train_TRR[-1]))
print('Original_Test_TRR: {0}, Adver_Test_TRR: {1}'.format(Original_Test_TRR, Test_TRR[-1]))
# Plot TRR
plt.figure()
plt.plot(range(epochs), Train_TRR, c='r', label='Training Set', linewidth=2)
plt.plot(range(epochs), Test_TRR, c='g', linestyle='--', label='Validation Set', linewidth=2)
plt.xlabel("Epoch")
plt.ylabel("TRR")
plt.legend()
plt.show()
if __name__ == '__main__':
malgan = MalGAN()
malgan.train(epochs=1000, batch_size=128)
我尝试更改输入形状，但仍然出现同样的错误。
如何查看输入数据的形状？已经提到过 11599,4。
在此代码中，
class MalGAN():
def __init__(self):
self.apifeature_dims = 128
self.z_dims = 20 #could try 20,malware
初始化时，z dims 设置为 20。并且 apifeature_dims 设置为 128
虽然错误指向相同的形状问题，
ValueError：“生成器”层的输入 1 与 层：预期形状=（无，128），发现形状=（32，20）
请将第3行提到的形状更改为self.z_dims = None
您的结果代码将是：
class MalGAN():
def __init__(self):
self.apifeature_dims = 128
self.z_dims = None #could try 20,malware