batch_size=32,
class_mode='binary')
model.fit(
training_set,
steps_per_epoch=70,
epochs=1,
validation_data=test_set,
validation_steps=10)
# In[15]:
#Saving and loading the model
model.save('face_recog_mobilenet.h5')
from keras.models import load_model
classifier = load_model('face_recog_mobilenet.h5')
# In[26]:
#Predicting Random Images using face recognization model
import os
import cv2
import numpy as np
from os import listdir
from os.path import isfile, join
model = MobileNet(input_shape=(128, 128, 3), include_top=False, pooling='avg')
x = model.output
x = Dense(512, activation="relu")(x)
predict = Dense(1, activation="sigmoid")(x)
model = Model(inputs=model.input, outputs=predict)
adam = Adam(lr=0.0001)
model.compile(optimizer=adam, loss="binary_crossentropy", metrics=["accuracy"])
model.summary()
history = model.fit(data_train,
epochs=10,
validation_data=data_test,
verbose=1)
model.save("/Clasificador.h5")
def visualizar_img_test(path, modelo):
img = cv2.imread(path)
img_1 = cv2.resize(img, (128, 128))
img = (img_1 / 255)
img = np.expand_dims(img, axis=0)
predict = modelo.predict(img)
if predict[0][0] < 0.7:
plt.title(str(100 - predict[0][0] * 100) + "% MASCARILLA")
plt.imshow(img_1.astype("uint8"))
plt.show()
else:
plt.title(str(predict[0][0] * 100) + "% NO TIENE MASCARILLA")
plt.imshow(img_1.astype("uint8"))
model_path = os.path.join(save_dir, model_name)
weight_path = os.path.join(save_dir, weight_name)
x_train, y_train, x_test, y_test = image_crop.read_data()
x_train_resized = image_crop.resize_imgs(x_train)
y = to_categorical(y_train, num_classes=34)
model = MobileNet(include_top=True,
weights=None,
classes=34,
pooling='max',
input_shape=(150, 150, 3))
checkpoint = ModelCheckpoint(filepath=os.path.join(
save_dir, 'MobileNet_weight.{epoch:02d}-{loss:.2f}.hdf5'),
verbose=1)
opt = RMSprop(lr=2e-5)
model.compile(optimizer=opt,
loss=losses.categorical_crossentropy,
metrics=[metrics.categorical_accuracy])
model.fit(x_train_resized,
y,
epochs=10,
batch_size=36,
callbacks=[checkpoint])
model.save(model_path)
model.save_weights(weight_path)
model.summary()
type(model)
x = model.layers[-6].output
predictions = Dense(29,activation='softmax')(x)
model = Model(inputs = model.input, outputs=predictions)
model.summary()
for layer in model.layers[:-23]:
layer.trainable = False
model.compile(Adam(lr = 0.001),loss = 'categorical_crossentropy',metrics = ['accuracy'])
model.fit_generator(train_batches, steps_per_epoch = 3627, validation_data = valid_batches, validation_steps = 723, epochs = 10, verbose = 2)
model.save('sign_language.h5')
####### model build and saved upto here
from keras.models import load_model
class SignLanguageModel():
Signs = ["A", "B","C","D","E","F","G","H","I","J","K","L","M","N","O","P","Q","R","S","T","U","V","W","X","Y","Z",
"del", "nothing",
"space"]
def __init__(self):
# load model from JSON file
self.model = load_model("sign_language.h5")
target_size=(img_rows,
img_cols),
class_mode='categorical')
validation_generator = validation_datagen.flow_from_directory(
validation_data_dir,
target_size=(img_rows, img_cols),
class_mode='categorical')
lr = 0.001
ep = 2
ol = train_generator.num_classes
model = MobileNet(weights='imagenet',
include_top=False,
input_shape=(img_rows, img_cols, 3))
model.save('MobileNet.h5')
for l in model.layers:
l.trainable = False
top_model = model.output
top_model = Flatten()(top_model)
top_model = Dense(512, activation='relu')(top_model)
top_model = Dense(1024, activation='relu')(top_model)
top_model = Dense(ol, activation='softmax')(top_model)
nmodel = Model(inputs=model.input, outputs=top_model)
nmodel.compile(loss='categorical_crossentropy',
optimizer='adam',
from keras.callbacks import Callback
from keras.models import Sequential
mobilenet_input_shape = (1080, 1920, 3)
""" Build a microclassifier (mobilenet -> microclassifier) """
# Load pre-trained mobilenet and set all layers to not be trainable
print("Loading weights from 224x224 MobileNet...", end=" ", flush=True)
mobilenet_base_model = MobileNet(input_shape=(224, 224, 3),
include_top=False,
weights='imagenet',
input_tensor=None,
pooling=None)
print("Done.")
print("Initializing {}x{} MobileNet...".format(*mobilenet_input_shape),
end=" ",
flush=True)
mobilenet_reshaped_model = MobileNet(input_shape=mobilenet_input_shape,
include_top=False,
weights=None,
input_tensor=None,
pooling=None)
print("Copying weights from 224x224 MobileNet...", end=" ", flush=True)
for reshaped_layer, layer in zip(mobilenet_reshaped_model.layers,
mobilenet_base_model.layers):
reshaped_layer.set_weights(layer.get_weights())
print("Setting all MobileNet layers to be non-trainable...", flush=True)
for layer in mobilenet_reshaped_model.layers:
layer.trainable = False
print("Done", flush=True)
mobilenet_reshaped_model.save("mobilenet.h5")
