def main():
if DO_TEST: test()
model = ResNet50(input_shape=(64, 64, 3), classes=6)
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset(
)
# Normalize image vectors
X_train = X_train_orig / 255.
X_test = X_test_orig / 255.
# Convert training and test labels to one hot matrices
Y_train = convert_to_one_hot(Y_train_orig, 6).T
Y_test = convert_to_one_hot(Y_test_orig, 6).T
print("number of training examples = " + str(X_train.shape[0]))
print("number of test examples = " + str(X_test.shape[0]))
print("X_train shape: " + str(X_train.shape))
print("Y_train shape: " + str(Y_train.shape))
print("X_test shape: " + str(X_test.shape))
print("Y_test shape: " + str(Y_test.shape))
model.fit(X_train, Y_train, epochs=7, batch_size=64)
my_preds = model.evaluate(X_test, Y_test)
print("My Loss = " + str(my_preds[0]))
print("My Test Accuracy = " + str(my_preds[1]))
model = load_model('../trained_model/ResNet50.h5')
preds = model.evaluate(X_test, Y_test)
print("Their Loss = " + str(preds[0]))
print("Their Test Accuracy = " + str(preds[1]))
def Load_Model(themodel):
model = load_model(themodel)
X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = resnets_utils.load_dataset()
# Normalize image vectors
X_train = X_train_orig / 255.
X_test = X_test_orig / 255.
# Convert training and test labels to one hot matrices
Y_train = resnets_utils.convert_to_one_hot(Y_train_orig, 6).T
Y_test = resnets_utils.convert_to_one_hot(Y_test_orig, 6).T
# 评估模型
preds = model.evaluate(X_test, Y_test)
print("误差值 = " + str(preds[0]))
print("准确率 = " + str(preds[1]))
return model
def Train_Model():
# print(datetime.datetime.now())
# 实体化并编译
model = ResNet50(input_shape=(64, 64, 3), classes=6)
model.compile(optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"])
X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = resnets_utils.load_dataset()
# Normalize image vectors
X_train = X_train_orig / 255.
X_test = X_test_orig / 255.
# Convert training and test labels to one hot matrices
Y_train = resnets_utils.convert_to_one_hot(Y_train_orig, 6).T
Y_test = resnets_utils.convert_to_one_hot(Y_test_orig, 6).T
print("number of training examples = " + str(X_train.shape[0]))
print("number of test examples = " + str(X_test.shape[0]))
print("X_train shape: " + str(X_train.shape))
print("Y_train shape: " + str(Y_train.shape))
print("X_test shape: " + str(X_test.shape))
print("Y_test shape: " + str(Y_test.shape))
"""
number of training examples = 1080
number of test examples = 120
X_train shape: (1080, 64, 64, 3)
Y_train shape: (1080, 6)
X_test shape: (120, 64, 64, 3)
Y_test shape: (120, 6)
"""
# 训练
model.fit(X_train, Y_train, epochs=40, batch_size=32)
# 评估模型
preds = model.evaluate(X_test, Y_test)
print("误差值 = " + str(preds[0]))
print("准确率 = " + str(preds[1]))
X = convolutional_block(X, 3, [512, 512, 2048], 5, 'a', s=2)
X = identity_block(X, 3, [256, 256, 2048], 5, 'b')
X = identity_block(X, 3, [256, 256, 2048], 5, 'c')
X = AveragePooling2D((2, 2), name='avg_pool')(X)
X = Flatten()(X)
output = Dense(classes, activation='softmax', name="fc" + str(classes))(X)
model = Model(inputs=input, outputs=output)
return model
if __name__ == '__main__':
X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = resnets_utils.load_dataset(
)
# Normalize image vectors
X_train = X_train_orig / 255.
X_test = X_test_orig / 255.
# Convert training and test labels to one hot matrices
Y_train = resnets_utils.convert_to_one_hot(Y_train_orig, 6).T
Y_test = resnets_utils.convert_to_one_hot(Y_test_orig, 6).T
print("number of training examples = " + str(X_train.shape[0]))
print("number of test examples = " + str(X_test.shape[0]))
print("X_train shape: " + str(X_train.shape))
print("Y_train shape: " + str(Y_train.shape))
print("X_test shape: " + str(X_test.shape))
print("Y_test shape: " + str(Y_test.shape))
# output layer
X = Flatten()(X)
X = Dense(classes, activation='softmax', name='fc' + str(classes), kernel_initializer=glorot_uniform(seed=0))(X)
# Create model
model = Model(inputs=X_input, outputs=X, name='ResNet50')
return model
model = ResNet50(input_shape=(64, 64, 3), classes=6)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()
# Normalize image vectors
X_train = X_train_orig / 255.
X_test = X_test_orig / 255.
# Convert training and test labels to one hot matrices
Y_train = convert_to_one_hot(Y_train_orig, 6).T
Y_test = convert_to_one_hot(Y_test_orig, 6).T
print("number of training examples = " + str(X_train.shape[0]))
print("number of test examples = " + str(X_test.shape[0]))
print("X_train shape: " + str(X_train.shape))
print("Y_train shape: " + str(Y_train.shape))
print("X_test shape: " + str(X_test.shape))
print("Y_test shape: " + str(Y_test.shape))
