After running fine tuning , but the accuracy became worse after training. I don’t know why.
My network is very simple, just one layer, the training set is mnist. After training, the weight didn’t change, but the bias changed significantly.

this is the model
inputs = Input((IMG_SIZEX, IMG_SIZEY))
x = Flatten()(inputs)
output = Dense(10, activation = ‘softmax’)(x)
model = Model(inputs, output)
optimizer = Adam(lr=0.001)
loss_fn = tf.keras.losses.CategoricalCrossentropy(from_logits=False)
model.compile(optimizer = optimizer,
loss = loss_fn, metrics=[‘accuracy’])
2)The fine tuning code is below.
def training_helper(sim, generator):
g = sim.session.graph
sess = sim.session
with g.as_default():
x = sim.session.graph.get_tensor_by_name(“input_2:0”)
y = g.get_tensor_by_name(“dense_1/Softmax:0”)
#fc1_w = g.get_tensor_by_name("/ReadVariableOp:0")
ce = g.get_tensor_by_name("loss/dense_1_loss/softmax_cross_entropy_with_logits/Reshape:0")
# Using Adam optimizer
train_step = tf.compat.v1.train.AdamOptimizer(1e3, name="TempAdam").minimize(ce)
graph_eval.initialize_uninitialized_vars(sess)
# Input data for MNIST
# Using 100 iterations and batch of size 100
for j in range(10):
for i in range(600):
inputdata= x_train[i*100:i*100+100,:,:]
outputdata = y_train[i*100:i*100+100]
outputdata = tf.keras.utils.to_categorical(outputdata, 10)
sess.run(train_step, feed_dict={x: inputdata, y:outputdata})