Merge pull request #6 from ahundt/learn_rate

automatically reduce learning rate

cifar10.py

@@ -1,5 +1,7 @@
 from __future__ import print_function
 
+import os.path
+
 import densenet
 import numpy as np
 import sklearn.metrics as metrics
@@ -8,12 +10,16 @@
 from keras.utils import np_utils
 from keras.preprocessing.image import ImageDataGenerator
 from keras.optimizers import Adam
-from keras.callbacks import ModelCheckpoint
+from keras.callbacks import CSVLogger, ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
 from keras import backend as K
 
+
+import tensorflow as tf
+import datetime
+
 batch_size = 64
 nb_classes = 10
-nb_epoch = 200
+nb_epoch = 300
 
 img_rows, img_cols = 32, 32
 img_channels = 3
@@ -53,14 +59,25 @@
 generator.fit(trainX, seed=0)
 
 # Load model
-model.load_weights("weights/DenseNet-40-12-CIFAR10.h5")
-print("Model loaded.")
-
-# model.fit_generator(generator.flow(trainX, Y_train, batch_size=batch_size), samples_per_epoch=len(trainX), nb_epoch=nb_epoch,
-#                    callbacks=[ModelCheckpoint("weights/DenseNet-40-12-CIFAR10.h5", monitor="val_acc", save_best_only=True,
-#                                               save_weights_only=True)],
-#                    validation_data=(testX, Y_test),
-#                    nb_val_samples=testX.shape[0], verbose=2)
+weights_file="weights/DenseNet-40-12CIFAR10-tf.h5"
+if os.path.exists(weights_file):
+    model.load_weights(weights_file)
+    print("Model loaded.")
+
+out_dir="weights/"
+
+lr_reducer      = ReduceLROnPlateau(monitor='val_loss', factor=np.sqrt(0.1), 
+                                    cooldown=0, patience=10, min_lr=0.5e-6)
+early_stopper   = EarlyStopping(monitor='val_acc', min_delta=0.0001, patience=20)
+model_checkpoint= ModelCheckpoint(weights_file, monitor="val_acc", save_best_only=True,
+                                  save_weights_only=True,mode='auto')
+
+callbacks=[lr_reducer,early_stopper,model_checkpoint]
+
+model.fit_generator(generator.flow(trainX, Y_train, batch_size=batch_size), samples_per_epoch=len(trainX), nb_epoch=nb_epoch,
+                   callbacks=callbacks,
+                   validation_data=(testX, Y_test),
+                   nb_val_samples=testX.shape[0], verbose=2)
 
 yPreds = model.predict(testX)
 yPred = np.argmax(yPreds, axis=1)