model_cnn.add(MaxPooling1D(1))
model_cnn.add(Flatten())
model_cnn.add(Dense(hidden_dim, activation='relu'))
model_cnn.add(Dropout(0.5))
model_cnn.add(Dense(numclass, activation='softmax'))
model_cnn.summary()
model_cnn.compile(loss='categorical_crossentropy',
optimizer=Adam(lr=learning_rate),
metrics=['acc'])
lr_level=2
best_score_by_acc = 0.
best_score_by_loss = 999.
x_val, y_val =dataset.get_all_validation_data()
for step in range(dataset.get_step()):
cur_step = str(step + 1) + "/" + str(dataset.get_step())
print('\n步骤'+cur_step)
x_train, y_train = dataset.next_train_batch()
this_epoch_loss_and_acc = model_cnn.fit(x=x_train, y=y_train, validation_data=(x_val, y_val), epochs=1,verbose=2)
for iters in range(numclass):
history_test = model_cnn.evaluate(
x=x_val_slice[iters],
y=y_val_slice[iters],
batch_size=None,
verbose=2
)
print('class-%d __ loss :%.4f , acc :%.4f' %(iters ,history_test[0],history_test[1]))
# save best acc
train_size = dataset.get_train_length()
val_size = dataset.get_validation_length()
print("train size:" + str(train_size))
print("test size:" + str(val_size))
steps_per_epoch = int((train_size - 1) / args.BATCH) + 1
print("steps_per_epoch:", steps_per_epoch)
def get_train_generator():
while 1:
yield dataset.next_train_batch()
train_generator = get_train_generator()
val_data = dataset.get_all_validation_data()
callbacks = []
ModelCheckpoint = keras.callbacks.ModelCheckpoint(os.path.join(
MODEL_PATH, KERAS_MODEL_NAME),
monitor='val_acc',
verbose=1,
save_best_only=True,
save_weights_only=False,
mode='auto',
period=1)
EarlyStopping = keras.callbacks.EarlyStopping(monitor='val_acc',
min_delta=0,
patience=config.es_patience,
verbose=1,
mode='auto',
