def __init__(self):
# load the trained model
self.model = load_model.get_model('')
self.model.compile(loss='categorical_crossentropy',
optimizer='adadelta',
metrics=['accuracy'])
def main(argv):
emot_dic = {"anger":0, "joy":1, "sadness":2, "fear":3}
x = tokenize(argv[0])
emot = np.array([0, 0, 0, 0])
emot[emot_dic[argv[1]]] = 1
model = get_model("model.h5")
out = model.predict([x.reshape((1, 50, 1)), emot.reshape((1, 4))])[0]
print(argv[1], ":", np.argmax(out))
def main(argv):
emot_dic = {"anger": 0, "joy": 1, "sadness": 2, "fear": 3}
model = get_model("model.h5")
for s in ["anger", "joy", "sadness", "fear"]:
df = pd.read_csv(argv[0],
sep='\t',
header=None,
encoding='utf-8',
quoting=3)
df.columns = ['id', 'text', 'polarity', 'class']
df = df[df["polarity"] == s]
test = np.array(df['text'])
test_type = np.array(df["polarity"])
X = []
for x in test:
X.append(tokenize(x))
X = np.array(X)
emot = np.zeros((len(test), 4))
for x in range(len(test_type)):
emot[x, emot_dic[test_type[x]]] = 1
out = model.predict(
[X.reshape((len(test), 50, 1)),
emot.reshape((len(test), 4))])
y_ = np.array(df["class"])
y = np.array([int(x[0]) for x in y_])
acc = np.count_nonzero(y == out.argmax(axis=1)) / float(
out.argmax(axis=1).shape[0])
print(s, acc)
df["class"] = out.argmax(axis=1)
df.to_csv("EI-oc_en_" + s + "_pred.txt",
sep='\t',
header=None,
index=None)
X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)
input_shape = (1, img_rows, img_cols)
else:
X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)
X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255
print('X_train shape:', X_train.shape)
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')
model = load_model.get_model(input_shape)
model.compile(loss='categorical_crossentropy',
optimizer='adadelta',
metrics=['accuracy'])
filepath = load_model.get_weights_file()
checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')
callbacks_list = [checkpoint]
model.fit(X_train, Y_train, batch_size=batch_size, epochs=nb_epoch, verbose=1, validation_data=(X_test,Y_test), callbacks=callbacks_list)
score = model.evaluate(X_test, Y_test, verbose=0)
predict = model.predict(X_test,batch_size = batch_size,verbose = 0)
# calculate the accuracy with the test data
