Question

我只是参加了我的第一个深度学习项目，以熟悉keras和tf。这是一个多标签分类问题，我使用一个简单的卷积网络从XRD模式中识别出各个化合物。

我花了一些时间，但结果确实让我震惊。我没想到我的简单网络在准确性方面会表现得那么好。

然后我意识到我一直在使用theano作为后端。因此，我再次尝试使用tensorflow，并且相同的输入数据给出了可怕的结果。甚至比随机森林分类器还差。

我几乎一无所知。我想我做错了，但我只是想不通....

无论如何，这是代码（输入数据在下面提供...）

Theano_vs_TF.py：

%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import os

os.environ['KERAS_BACKEND']='theano'

from keras.models import Model
from keras.models import Sequential
import keras as K

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

X=np.loadtxt('X.txt')
Y=np.loadtxt('Y.txt')

X3d = np.expand_dims(X, axis=-1)

# start with TF-model
tf_model = keras.Sequential()
wreg = tf.keras.regularizers.l2(l=0) # dont use for now

tf_model.add(layers.Conv1D(32, 8,strides=8, padding='same',
                        input_shape=(180,1), activation='relu',kernel_regularizer=wreg))

tf_model.add(layers.Conv1D(32, 5,strides=5, padding='same', 
                        activation='relu',kernel_regularizer=wreg))

tf_model.add(layers.Conv1D(16, 3,strides=3, padding='same', 
                        activation='relu',kernel_regularizer=wreg))

tf_model.add(layers.Flatten())
tf_model.add(layers.Dense(512,activation='relu',kernel_regularizer=wreg))
tf_model.add(layers.Dense(29, activation='sigmoid',kernel_regularizer=wreg))

tf_optimizer = tf.keras.optimizers.Adam(learning_rate=1e-3)

tf_model.compile(loss='binary_crossentropy',
              optimizer=tf_optimizer,
              metrics=['accuracy'],
              )

tf_model.summary()

# train tf-model
tf_history = tf_model.fit(X3d,Y,batch_size=128, 
                    epochs=50, verbose=1,validation_split=0.3,
                   )

plt.plot(tf_history.history['accuracy'])
plt.plot(tf_history.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'val'], loc='upper left')
plt.show()

# now compare to theano model
th_model = Sequential() #old way
wreg = K.regularizers.l2(l=0.0) # 1e-3 was way too much, no hints for overfitting so far

th_model.add(K.layers.Conv1D(32, 8,strides=8, padding='same',
                          input_shape=(180,1), activation='relu', 
                          kernel_regularizer=wreg))

th_model.add(K.layers.Conv1D(32, 5,strides=5, 
                          padding='same', activation='relu',kernel_regularizer=wreg))

th_model.add(K.layers.Conv1D(16, 3,strides=3, padding='same', activation='relu',kernel_regularizer=wreg))
th_model.add(K.layers.Flatten())
th_model.add(K.layers.Dense(512,activation='relu',kernel_regularizer=wreg))
th_model.add(K.layers.Dense(29, activation='sigmoid'))


#Define optimizer        
optimizer = K.optimizers.adam(lr=1e-3)

# Compile model
th_model.compile(loss='binary_crossentropy',
              optimizer=optimizer,
              metrics=['accuracy'],

              )

th_model.summary()

# train th model and plot metrics
th_history = th_model.fit(X3d,Y,batch_size=128, 
                    epochs=50, verbose=1,validation_split=0.3,
                   )

plt.plot(th_history.history['acc'])
plt.plot(th_history.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'val'], loc='upper left')
plt.show()

Uguu链接（将在24小时后失效） X.tar.gz Y.tar.gz

Tensorflow学习曲线：

Theano学习曲线：

为什么keras theano后端的性能优于张量流？

0 个答案: