我在具有相同参数和相同数据集（MNIST）的TensorFlow与Keras之间进行了直接比较。
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, kernel_initializer='normal', activation='relu'))
model.add(Dense(10, kernel_initializer='normal', 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, [None, 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), tf.argmax(y, 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内容。谁能解释这种矛盾？