Keras2 対応

keras/mcz_main.py

@@ -2,7 +2,7 @@
 from keras.preprocessing.image import ImageDataGenerator
 from keras.models import Sequential
 from keras.layers import Dense, Dropout, Activation, Flatten
-from keras.layers import Convolution2D, MaxPooling2D
+from keras.layers import Conv2D, MaxPooling2D
 from keras.utils import np_utils
 import mcz_input
 import sys
@@ -28,17 +28,16 @@
 
 model = Sequential()
 
-model.add(Convolution2D(32, 3, 3, border_mode='same',
-                        input_shape=X_train.shape[1:]))
+model.add(Conv2D(32, (3, 3), padding='same', input_shape=X_train.shape[1:]))
 model.add(Activation('relu'))
-model.add(Convolution2D(32, 3, 3))
+model.add(Conv2D(32, (3, 3)))
 model.add(Activation('relu'))
 model.add(MaxPooling2D(pool_size=(2, 2)))
 model.add(Dropout(0.25))
 
-model.add(Convolution2D(64, 3, 3, border_mode='same'))
+model.add(Conv2D(64, (3, 3), padding='same'))
 model.add(Activation('relu'))
-model.add(Convolution2D(64, 3, 3))
+model.add(Conv2D(64, (3, 3)))
 model.add(Activation('relu'))
 model.add(MaxPooling2D(pool_size=(2, 2)))
 model.add(Dropout(0.25))
@@ -86,11 +85,9 @@
     datagen.fit(X_train)
 
     # Fit the model on the batches generated by datagen.flow().
-    model.fit_generator(datagen.flow(X_train, Y_train,
-                                     batch_size=batch_size),
-                        samples_per_epoch=X_train.shape[0],
-                        nb_epoch=nb_epoch,
+    model.fit_generator(datagen.flow(X_train, Y_train, batch_size=batch_size),
+                        steps_per_epoch=len(X_train),
+                        epochs=nb_epoch,
                         validation_data=(X_test, Y_test))
 
-
 model.save('model.h5')

keras/packages.txt

@@ -0,0 +1,18 @@
+backports.weakref==1.0rc1
+bleach==1.5.0
+coremltools==0.3.0
+funcsigs==1.0.2
+h5py==2.7.0
+html5lib==0.9999999
+Keras==2.0.0
+Markdown==2.2.0
+mock==2.0.0
+numpy==1.13.0
+pbr==3.1.1
+protobuf==3.3.0
+PyYAML==3.12
+scipy==0.19.1
+six==1.10.0
+tensorflow==1.2.0
+Theano==0.9.0
+Werkzeug==0.12.2