Why keras theano backend outperforms tensorflow? - tensorflow

Question

I just dove into my first deep-learning project to get familiar with keras and tf.
It is a multi-label classification problem where I'm using a simple convolutional network to identify individual chemical compounds from XRD-patterns.
It took me some time but the results really blew my mind. I did not expect my simple network to perform that good in terms of accuracy.
Then I realized that I've been using theano as backend.
So I gave it another try with tensorflow and the very same input data gave terrible results. Even worse than a random forest classifier.
I'm pretty much clueless what is going on there.
I guess I'm doing something wrong, but I just couldn't figure out what ....
Anyway, here's the code (the input data is provided below...)
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 links (will expire after 24h)
X.tar.gz
Y.tar.gz
Tensorflow learning curve:
Theano learning curve: