我直接比较了具有相同参数和相同数据集（MNIST）的TensorFlow与Keras。

奇怪的是Keras在10个周期内实现96％的性能，而TensorFlow在10个周期内实现约70％的性能。我在同一实例中多次运行了此代码，并且总是会出现这种不一致的情况。

即使为TensorFlow设置了50个纪元，最终性能也达到了90％。

代码：

import keras
from keras.datasets import mnist

(x_train,y_train),(x_test,y_test) = mnist.load_data()

# One hot encoding
from keras.utils import np_utils
y_train = np_utils.to_categorical(y_train) 
y_test = np_utils.to_categorical(y_test) 

# Changing the shape of input images and normalizing
x_train = x_train.reshape((60000,784))
x_test = x_test.reshape((10000,784))
x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255

import keras
from keras.models import Sequential
from keras.layers import Dense,Dropout,activation

# Creating the neural network
model = Sequential()
model.add(Dense(30,input_dim=784,kernel_initializer='normal',activation='relu'))
model.add(Dense(30,activation='relu'))
model.add(Dense(10,activation='softmax'))

# Optimizer
optimizer = keras.optimizers.Adam()

# Loss function
model.compile(loss='categorical_crossentropy',optimizer=optimizer,metrics=['acc'])

# Training
model.fit(x_train,y_train,epochs=10,batch_size=200,validation_data=(x_test,y_test),verbose=1)

# Checking the final accuracy
accuracy_final = model.evaluate(x_test,y_test,verbose=0)
print('Model accuracy: ',accuracy_final)

TensorFlow代码：（x_train，x_test，y_train，y_test与上面的Keras代码的输入相同）

import tensorflow as tf
# Epochs parameters
epochs = 10
batch_size = 200

# Neural network parameters
n_input = 784 
n_hidden_1 = 30 
n_hidden_2 = 30 
n_classes = 10 

# Placeholders x,y
x = tf.placeholder(tf.float32,[None,n_input])
y = tf.placeholder(tf.float32,n_classes])

# Creating the first layer
w1 = tf.Variable(tf.random_normal([n_input,n_hidden_1]))
b1 = tf.Variable(tf.random_normal([n_hidden_1]))
layer_1 = tf.nn.relu(tf.add(tf.matmul(x,w1),b1)) 

# Creating the second layer 
w2 = tf.Variable(tf.random_normal([n_hidden_1,n_hidden_2]))
b2 = tf.Variable(tf.random_normal([n_hidden_2]))
layer_2 = tf.nn.relu(tf.add(tf.matmul(layer_1,w2),b2)) 

# Creating the output layer 
w_out = tf.Variable(tf.random_normal([n_hidden_2,n_classes]))
bias_out = tf.Variable(tf.random_normal([n_classes]))
output = tf.matmul(layer_2,w_out) + bias_out

# Loss function
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = output,labels = y))
# Optimizer
optimizer = tf.train.AdamOptimizer().minimize(cost)

# Making predictions
predictions = tf.equal(tf.argmax(output,1),tf.argmax(y,1))

# accuracy
accuracy = tf.reduce_mean(tf.cast(predictions,tf.float32))

# Variables that will be used in the training cycle
train_size = x_train.shape[0]
total_batches = train_size / batch_size

# Initializing the variables
init = tf.global_variables_initializer()

# Opening the session
with tf.Session() as sess:
    sess.run(init)

    # Training cycle
    for epoch in range(epochs):

        # Loop through all batch iterations
        for i in range(0,train_size,batch_size): 
            batch_x = x_train[i:i + batch_size]
            batch_y = y_train[i:i + batch_size]

            # Fit training
            sess.run(optimizer,feed_dict={x: batch_x,y: batch_y})

        # Running accuracy (with test data) on each epoch    
        acc_val = sess.run(accuracy,feed_dict={x: x_test,y: y_test})
        # Showing results after each epoch
        print ("Epoch: ","{}".format((epoch + 1)))
        print ("accuracy_val = ","{:.3f}".format(acc_val))

    print ("Training Completed!")

    # Checking the final accuracy
    checking = tf.equal(tf.argmax(output,1))
    accuracy_final = tf.reduce_mean(tf.cast(checking,tf.float32))  
    print ("Model accuracy:",accuracy_final.eval({x: x_test,y: y_test}))

我正在同一实例中运行所有程序。谁能解释这种矛盾？