DNN version with same # of parameter as CNN

hw3/test_dnn.py

@@ -0,0 +1,46 @@
+#!/usr/bin/env python
+import sys, os
+import numpy as np
+import keras
+from keras.models import load_model
+from keras.utils import plot_model
+
+def ensure_dir(file_path):
+ directory = os.path.dirname(file_path)
+ if len(directory) == 0: return
+ if not os.path.exists(directory):
+ os.makedirs(directory)
+
+# Parameter
+height = width = 48
+num_classes = 7
+input_shape = (height*width, )
+model_name = 'dnn1.h5'
+
+# Read the test data
+with open(sys.argv[1], "r+") as f:
+ line = f.read().strip().replace(',', ' ').split('\n')[1:]
+ raw_data = ' '.join(line)
+ length = width*height+1 #1 is for label
+ data = np.array(raw_data.split()).astype('float').reshape(-1, length)
+ X = data[:, 1:]
+ X /= 255
+
+# Load model
+model = load_model(model_name)
+
+# Predict the test data
+ans = model.predict_classes(X)
+ans = list(ans)
+
+# Write prediction
+## check the folder of out.csv is exist; otherwise, make it
+ensure_dir(sys.argv[2])
+
+result = []
+for index, value in enumerate(ans):
+ result.append("{0},{1}".format(index, value))
+
+with open(sys.argv[2], "w+") as f:
+ f.write("id,label\n")
+ f.write("\n".join(result))

hw3/train_dnn.py

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+#!/usr/bin/env python
+import sys, os
+import numpy as np
+from keras.utils import to_categorical
+from keras.preprocessing.image import ImageDataGenerator
+from keras.models import Sequential
+from keras.layers import Dense, Activation, Flatten, Dropout, LeakyReLU
+from keras.layers import BatchNormalization
+from keras import losses
+from keras import optimizers
+from keras.callbacks import CSVLogger, ModelCheckpoint
+
+# Parameter
+height = width = 48
+num_classes = 7
+input_shape = (height, width, 1)
+batch_size = 128
+epochs = 300
+zoom_range = 0.2
+model_name = 'dnn1.h5'
+isValid = 1
+
+# Read the train data
+with open(sys.argv[1], "r+") as f:
+ line = f.read().strip().replace(',', ' ').split('\n')[1:]
+ raw_data = ' '.join(line)
+ length = width*height+1 #1 is for label
+ data = np.array(raw_data.split()).astype('float').reshape(-1, length)
+ X = data[:, 1:]
+ Y = data[:, 0]
+ X /= 255
+ Y = Y.reshape(Y.shape[0], 1)
+ Y = to_categorical(Y, num_classes)
+
+# Change data into pictures format for generator
+X = X.reshape(X.shape[0], height, width, 1)
+
+# Split data
+if isValid:
+ valid_num = 3000
+ X_train, Y_train = X[:-valid_num], Y[:-valid_num]
+ X_valid, Y_valid = X[:-valid_num], Y[:-valid_num]
+else:
+ X_train, Y_train = X, Y
+
+# Image PreProcessing
+train_gen = ImageDataGenerator(rotation_range=25,
+ width_shift_range=0.1,
+ height_shift_range=0.1,
+ shear_range=0.1,
+ zoom_range=[1-zoom_range, 1+zoom_range],
+ horizontal_flip=True)
+train_gen.fit(X_train)
+
+
+# Construct the model
+model = Sequential()
+model.add(Flatten(input_shape=input_shape))
+
+model.add(Dense(1024))
+model.add(LeakyReLU(alpha=0.03))
+model.add(BatchNormalization())
+model.add(Dropout(0.5))
+
+model.add(Dense(512))
+model.add(LeakyReLU(alpha=0.03))
+model.add(BatchNormalization())
+model.add(Dropout(0.5))
+
+model.add(Dense(num_classes))
+model.add(Activation('softmax'))
+
+model.summary()
+
+# Compile the model
+model.compile(loss='categorical_crossentropy',
+ optimizer='adam',
+ metrics=['accuracy'])
+
+# Callbacks
+callbacks = []
+modelcheckpoint = ModelCheckpoint('dnn/weights.{epoch:03d}-{val_acc:.5f}.h5', monitor='val_acc', save_best_only=True)
+callbacks.append(modelcheckpoint)
+csv_logger = CSVLogger('dnn_log.csv', separator=',', append=False)
+callbacks.append(csv_logger)
+
+# Fit the model
+if isValid:
+ model.fit_generator(train_gen.flow(X_train, Y_train, batch_size=batch_size),
+ steps_per_epoch=10*X_train.shape[0]//batch_size,
+ epochs=epochs,
+ callbacks=callbacks,
+ validation_data=(X_valid, Y_valid))
+else:
+ model.fit_generator(train_gen.flow(X_train, Y_train, batch_size=batch_size),
+ steps_per_epoch=10*X_train.shape[0]//batch_size,
+ epochs=epochs,
+ callbacks=callbacks)
+
+# Save model
+model.save(model_name)