train

+#한번 한 다음에는 from kera.models import load_model 이용해서 모델 가지고와서 다시학습,,,,,그거 구현,,
+
+import os
+import numpy as np
+np.random.seed(123)
+from six.moves import cPickle
+
+from keras import backend as K
+from keras.models import Model
+from keras.layers import Input, Dense, Flatten
+from keras.layers import LSTM
+from keras.layers import TimeDistributed
+from keras.callbacks import LearningRateScheduler, ModelCheckpoint
+from keras.optimizers import Adam
+
+from prednet import PredNet
+from data_utils import SequenceGenerator
+from setting import *
+
+save_model = True  # if weights will be saved
+weights_file = os.path.join(WEIGHTS_DIR, 'prednet_weights.hdf5')  # where weights will be saved
+json_file = os.path.join(WEIGHTS_DIR, 'prednet_model.json')
+
+# Data files
+train_file = os.path.join(DATA_DIR, 'X_train.hkl')
+train_sources = os.path.join(DATA_DIR, 'sources_train.hkl')
+val_file = os.path.join(DATA_DIR, 'X_val.hkl')
+val_sources = os.path.join(DATA_DIR, 'sources_val.hkl')
+
+# Training parameters
+nb_epoch = 150
+batch_size = 10
+samples_per_epoch = 900
+N_seq_val = 100  # number of sequences to use for validation
+
+
+# Model parameters
+n_channels, im_height, im_width = (3, 128, 160)
+input_shape = (n_channels, im_height, im_width) if K.image_data_format() == 'channels_first' else (im_height, im_width, n_channels)
+stack_sizes = (n_channels, 48, 96, 192)
+R_stack_sizes = stack_sizes
+A_filt_sizes = (3, 3, 3)
+Ahat_filt_sizes = (3, 3, 3, 3)
+R_filt_sizes = (3, 3, 3, 3)
+layer_loss_weights = np.array([1., 0., 0., 0.])  # weighting for each layer in final loss; "L_0" model:  [1, 0, 0, 0], "L_all": [1, 0.1, 0.1, 0.1]
+layer_loss_weights = np.expand_dims(layer_loss_weights, 1)
+nt = 10  # number of timesteps used for sequences in training
+time_loss_weights = 1./ (nt - 1) * np.ones((nt,1))  # equally weight all timesteps except the first
+time_loss_weights[0] = 0
+
+
+prednet = PredNet(stack_sizes, R_stack_sizes,
+                  A_filt_sizes, Ahat_filt_sizes, R_filt_sizes,
+                  output_mode='error', return_sequences=True)
+
+#모델만들기
+inputs = Input(shape=(nt,) + input_shape)
+errors = prednet(inputs)  # errors will be (batch_size, nt, nb_layers)
+# TimeDistributed - LSTM이 many-to-many로 동작
+#                   매 스텝마다 cost가 계산되고 하위 스텝으로 오류가 전파
+errors_by_time = TimeDistributed(Dense(1, trainable=False), weights=[layer_loss_weights, np.zeros(1)], trainable=False)(errors)  # calculate weighted error by layer
+errors_by_time = Flatten()(errors_by_time)  # will be (batch_size, nt)
+final_errors = Dense(1, weights=[time_loss_weights, np.zeros(1)], trainable=False)(errors_by_time)  # weight errors by time
+model = Model(inputs=inputs, outputs=final_errors)
+model.compile(loss='mean_absolute_error', optimizer='adam')
+
+
+train_generator = SequenceGenerator(train_file, train_sources, nt, batch_size=batch_size, shuffle=True)
+val_generator = SequenceGenerator(val_file, val_sources, nt, batch_size=batch_size, N_seq=N_seq_val)
+
+
+lr_schedule = lambda epoch: 0.001 if epoch < 75 else 0.0001    # start with lr of 0.001 and then drop to 0.0001 after 75 epochs
+# 여기 콜백함수기능: 일정 구간이나 특정 accuracy threshold를 초과할 때 모델에서 checkpointing
+callbacks = [LearningRateScheduler(lr_schedule)]
+if save_model:
+    if not os.path.exists(WEIGHTS_DIR): os.mkdir(WEIGHTS_DIR)
+    callbacks.append(ModelCheckpoint(filepath=weights_file, monitor='val_loss', save_weights_only=True))
+# ModelCheckpoint
+# 상대적으로 큰 데이터셋을 학습할 때, 빈번하게 모델의 체크포인트를 저장하는 것은 매우 중요
+# monitor='val_loss',   val_loss 값이 개선되었을때 호출됩니다
+# save_best_only=True,  가장 best 값만 저장합니다
+
+history = model.fit_generator(train_generator, int(samples_per_epoch / batch_size), nb_epoch, callbacks=callbacks,
+                validation_data=val_generator, validation_steps=int(N_seq_val / batch_size))
+
+if save_model:
+    json_string = model.to_json()
+    with open(json_file, "w") as f:
+        f.write(json_string)
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