# layer.trainable = False
top_model = model.output
top_model = Dropout(0.5, name='drop9')(top_model)
top_model = Flatten()(top_model)
top_model = Dense(600)(top_model)
top_model = BatchNormalization()(top_model)
top_model = Activation('relu', name='relu_conv9')(top_model)
# top_model = Dense(600)(top_model)
# top_model = Activation('relu', name='relu_conv10')(top_model)
top_model = Dense(10)(top_model)
top_model = BatchNormalization()(top_model)
top_model = Activation('softmax', name='loss')(top_model)
top_model = Model(model.input, top_model, name='top_net')
top_model.load_weights(top_model_weights_path)
#設定model參數
top_model.compile(loss=keras.losses.categorical_crossentropy,
optimizer='adam',
metrics=['accuracy'])
#設定訓練紀錄
history = LossHistory(model, x_test, y_test)
#開始訓練
top_model.fit_generator(
generator=train_flow,
steps_per_epoch=30000 // 20,
verbose=2,
validation_data=train_flow,
validation_steps=5000 // 32,
epochs=30,
callbacks=[history]
def main2(X, X_te, y_te, words=None, tags=None, idx2word=None, idx2tag=None):
from k.models import model_from_json
from k.models import load_model
# load json and create model
# json_file = open('model_architecture.json', 'r')
# loaded_model_json = json_file.read()
# json_file.close()
# loaded_model = model_from_json(loaded_model_json)
# # load weights into new model
# loaded_model.load_weights("model_weights.h5")
# print("Loaded model from disk")
# loaded_model.compile(optimizer="rmsprop", loss="categorical_crossentropy", metrics=["accuracy"])
# # load json and create model
# with open('model.json', 'r') as json_file:
# loaded_model_json = json_file.read()
# loaded_model = model_from_json(loaded_model_json)
# # load weights into new model
# loaded_model.load_weights("model.h5")
# # print("Loaded model from disk")
# # Model reconstruction from JSON file
# with open('model_architecture.json', 'r') as f:
# loaded_model = model_from_json(f.read())
# Load weights into the new model
# loaded_model.load_weights('model_weights.h5')
# evaluate loaded model on test data
# loaded_model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
# loaded_model.compile(optimizer="rmsprop", loss="categorical_crossentropy", metrics=["accuracy"])
# loaded_model = load_model("My_Custom_Model3.h5")
from keras_contrib.layers import CRF
max_len = 50
n_tags = len(tags)
n_words = len(words)
word_input = Input(shape=(max_len, ))
word_emb = Embedding(input_dim=n_words + 1,
output_dim=20,
input_length=max_len,
mask_zero=True)(word_input)
model = Dropout(0.1)(word_emb)
model = Bidirectional(
LSTM(50, return_sequences=True, recurrent_dropout=0.1))(model)
model = TimeDistributed(Dense(50, activation="relu"))(model)
crf = CRF(n_tags)
out = crf(model)
model = Model(inputs=word_input, outputs=out)
model.compile(optimizer="rmsprop",
loss=crf.loss_function,
metrics=[crf.accuracy])
model.load_weights("model_weights.h5")
"""from keras.models import load_model
loaded_model = load_model("My_Custom_Model.h5")
print("Model Loaded.. Evaluating")
# score = loaded_model.evaluate([X,second_input],Y, verbose=1)
score = loaded_model.evaluate(X,Y, verbose=1)
#print accuracy
print("%s: %.2f%%" % (loaded_model.metrics_names[1], score[1]*100))
if words is not None and tags is not None:
i = 2318
p = loaded_model.predict(np.array([X[i]]))
# p = loaded_model.predict([np.array(X[i]),second_input[i]])
p = np.argmax(p, axis=-1)
print("{:15} ({:5}): {}".format("Word", "True", "Pred"))
for w, pred in zip(X[i], p[0]): # p[0] = p[[1,3,4,5]]
print("{:15}: {}".format(words[w], tags[pred]))
# print("%s: %.2f%%" % (loaded_model.metrics_names[1], score[1]*100))
# for x in score:
# print(x)
"""
# from keras.models import load_model
# loaded_model = load_model("My_Custom_Model3.h5")
# loaded_model = ""
# save_load_utils.load_all_weights(model, "Model_saved_using_contrib.h5")
print("Model Loaded.. Evaluating again")
score = model.evaluate(X_te, np.array(y_te), verbose=1)
print("%s: %.2f%%" % (model.metrics_names[1], score[1] * 100))
# score = model.evaluate(X_te, np.array(y_te), verbose=1)
# score = loaded_model.evaluate(X,Y, verbose=1)
#print accuracy
# print("%s: %.2f%%" % (loaded_model.metrics_names[1], score[1]*100))
# if words is not None and tags is not None:
# i = 2318
# p = loaded_model.predict(np.array([X[i]]))
# # p = loaded_model.predict([np.array(X[i]),second_input[i]])
# p = np.argmax(p, axis=-1)
# print("{:15} ({:5}): {}".format("Word", "True", "Pred"))
# for w, pred in zip(X[i], p[0]): # p[0] => p[[1,3,4,5]]
# print("{:15}: {}".format(words[w], tags[pred]))
loaded_model = model
i = 1
for x in X_te[i]:
print(idx2word[x], end=" ")
print(" ")
if words is not None and tags is not None:
p = loaded_model.predict(np.array([X_te[i]]))
p = np.argmax(p, axis=-1)
true = np.argmax(y_te[i], axis=-1)
print("{:15} ({:5}): {}".format("Word", "True", "Pred"))
print("{}th training example:".format(i))
for w, t, pred in zip(X_te[i], true, p[0]):
if w != 0:
print("{:15}: {:10} {}".format(idx2word[w - 1], idx2tag[t],
idx2tag[pred]))
print('*' * 50)
print(p)
print("len(p) = ", len(p))
def genderPredictionModel(h, w):
inp_img = Input((h, w, 3))
model = ZeroPadding2D((1,1),input_shape=(224,224, 3))(inp_img)
model = Convolution2D(64, (3, 3), activation='relu')(model)
model = ZeroPadding2D((1,1))(model)
model = Convolution2D(64, (3, 3), activation='relu')(model)
model = MaxPooling2D((2,2), strides=(2,2))(model)
model = ZeroPadding2D((1,1))(model)
model = Convolution2D(128, (3, 3), activation='relu')(model)
model = ZeroPadding2D((1,1))(model)
model = Convolution2D(128, (3, 3), activation='relu')(model)
model = MaxPooling2D((2,2), strides=(2,2))(model)
model = ZeroPadding2D((1,1))(model)
model = Convolution2D(256, (3, 3), activation='relu')(model)
model = ZeroPadding2D((1,1))(model)
model = Convolution2D(256, (3, 3), activation='relu')(model)
model = ZeroPadding2D((1,1))(model)
model = Convolution2D(256, (3, 3), activation='relu')(model)
model = MaxPooling2D((2,2), strides=(2,2))(model)
model = ZeroPadding2D((1,1))(model)
model = Convolution2D(512, (3, 3), activation='relu')(model)
model = ZeroPadding2D((1,1))(model)
model = Convolution2D(512, (3, 3), activation='relu')(model)
model = ZeroPadding2D((1,1))(model)
model = Convolution2D(512, (3, 3), activation='relu')(model)
model = MaxPooling2D((2,2), strides=(2,2))(model)
model = ZeroPadding2D((1,1))(model)
model = Convolution2D(512, (3, 3), activation='relu')(model)
model = ZeroPadding2D((1,1))(model)
model = Convolution2D(512, (3, 3), activation='relu')(model)
model = ZeroPadding2D((1,1))(model)
model = Convolution2D(512, (3, 3), activation='relu')(model)
model = MaxPooling2D((2,2), strides=(2,2))(model)
model = Convolution2D(4096, (7, 7), activation='relu')(model)
model = Dropout(0.5)(model)
model = Convolution2D(4096, (1, 1), activation='relu')(model)
model = Dropout(0.5)(model)
model = Convolution2D(2622, (1, 1))(model)
model = Flatten()(model)
model = Activation('softmax')(model)
model = Model(inp_img, model)
# return Model(inp_img, model)
model.load_weights('vgg_face_weights.h5')
for layer in model.layers[:-7]:
layer.trainable = False
base_model_output = Sequential()
base_model_output = Convolution2D(2, (1, 1), name='predictions')(model.layers[-4].output)
base_model_output = Flatten()(base_model_output)
base_model_output = Activation('softmax')(base_model_output)
genderModel = Model(inputs=model.input, outputs=base_model_output)
genderModel.load_weights("gender_model_weights.h5")
return genderModel
