add in trained models for the binary classifier

Author: chen0040

keras_image_classifier/cnn_bi_classifier_train.py

@@ -64,7 +64,7 @@
     class_mode='binary'
 )
 
-model.fit_generator(train_datagen,
+model.fit_generator(train_generator,
                     steps_per_epoch=nb_train_samples // batch_size,
                     epochs=epochs,
                     validation_data=validation_generator,

keras_image_classifier/cnn_multi_classifier_train.py

@@ -0,0 +1,83 @@
+from keras.models import Sequential
+from keras.layers import Conv2D, MaxPooling2D, Dense, Activation, Flatten, Dropout
+from keras.preprocessing.image import ImageDataGenerator
+import keras.backend as K
+
+train_data_dir = 'multi_classifier_data/training'
+validation_data_dir = 'multi_classifier_data/validation'
+img_width, img_height = 150, 150
+batch_size = 16
+epochs= 50
+nb_classes = 10
+nb_train_samples = 2000
+nb_validation_samples = 800
+
+train_datagen = ImageDataGenerator(
+    rescale=1. / 255,
+    shear_range=0.2,
+    horizontal_flip=True,
+    zoom_range=0.2
+)
+
+validation_datagen = ImageDataGenerator(
+    rescale=1. / 255,
+)
+
+train_generator = train_datagen.flow_from_directory(
+    directory=train_data_dir,
+    class_mode='categorical',
+    classes=nb_classes,
+    batch_size=batch_size
+)
+
+validation_generator = validation_datagen.flow_from_directory(
+    directory=validation_data_dir,
+    class_mode='categorical',
+    classes=nb_classes,
+    batch_size=batch_size
+)
+
+if K.image_data_format() == 'channels_first':
+    input_shape = (3, img_width, img_height)
+else:
+    input_shape = (img_width, img_height, 3)
+
+model = Sequential()
+model.add(Conv2D(filters=32, input_shape=input_shape, kernel_size=(3, 3)))
+model.add(Activation('relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+
+model.add(Conv2D(filters=32, kernel_size=(3, 3)))
+model.add(Activation('relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+
+model.add(Dropout(rate=0.25))
+
+model.add(Conv2D(filters=64, kernel_size=(3, 3)))
+model.add(Activation('relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+
+model.add(Conv2D(filters=64, kernel_size=(3, 3)))
+model.add(Activation('relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+
+model.add(Dropout(rate=0.25))
+
+model.add(Flatten())
+model.add(Dense(units=64))
+model.add(Activation('relu'))
+model.add(Dropout(rate=0.5))
+model.add(Dense(units=nb_classes))
+model.add(Activation('softmax'))
+
+model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
+model.fit_generator(generator=train_generator,
+                    steps_per_epoch=nb_train_samples // batch_size,
+                    epochs=epochs,
+                    validation_generator=validation_generator,
+                    validation_steps=nb_validation_samples // batch_size)
+
+json = model.to_json()
+open('models/cnn_multi_classifier_architecture.json', 'w').write(json)
+model.save_weights('models/cnn_multi_classifier_weights.h5', overwrite=True)
+

keras_image_classifier/models/cnn_bi_classifier_architecture.json

@@ -0,0 +1 @@
+{"class_name": "Sequential", "config": [{"class_name": "Conv2D", "config": {"name": "conv2d_1", "trainable": true, "batch_input_shape": [null, 150, 150, 3], "dtype": "float32", "filters": 32, "kernel_size": [3, 3], "strides": [1, 1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1, 1], "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Activation", "config": {"name": "activation_1", "trainable": true, "activation": "relu"}}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_1", "trainable": true, "pool_size": [2, 2], "padding": "valid", "strides": [2, 2], "data_format": "channels_last"}}, {"class_name": "Conv2D", "config": {"name": "conv2d_2", "trainable": true, "filters": 32, "kernel_size": [3, 3], "strides": [1, 1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1, 1], "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Activation", "config": {"name": "activation_2", "trainable": true, "activation": "relu"}}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_2", "trainable": true, "pool_size": [2, 2], "padding": "valid", "strides": [2, 2], "data_format": "channels_last"}}, {"class_name": "Conv2D", "config": {"name": "conv2d_3", "trainable": true, "filters": 64, "kernel_size": [3, 3], "strides": [1, 1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1, 1], "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Activation", "config": {"name": "activation_3", "trainable": true, "activation": "relu"}}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_3", "trainable": true, "pool_size": [2, 2], "padding": "valid", "strides": [2, 2], "data_format": "channels_last"}}, {"class_name": "Flatten", "config": {"name": "flatten_1", "trainable": true}}, {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "units": 64, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Dropout", "config": {"name": "dropout_1", "trainable": true, "rate": 0.5}}, {"class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "units": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Activation", "config": {"name": "activation_4", "trainable": true, "activation": "sigmoid"}}], "keras_version": "2.0.5", "backend": "tensorflow"}