client1lstm.py

from keras_preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
import seaborn as sns
from pathlib import Path
from tensorflow.keras.models import Sequential
from keras.layers import Dense, Activation, Dropout, Flatten, MaxPooling2D, Conv2D, MaxPool2D, BatchNormalization, \
    AveragePooling2D, RepeatVector
import cv2, pathlib, splitfolders
from tensorflow.keras.models import load_model
from tensorflow.python.keras.saving.saved_model.load import recurrent

model=Sequential()
model.add(Conv2D(64,(3,3),input_shape=(150,150,3)))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(128,(3,3)))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(128,(3,3)))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.2))


model.add(Flatten())
model.add(Dropout(0.5))
model.add(Dense(512))
model.add(Activation("relu"))
model.add(Dropout(0.4))
model.add(RepeatVector(13))
model.add(recurrent.LSTM(128, return_sequences=True, input_shape=(150,150, 3), activation='relu'))
model.add(recurrent.LSTM(64))
model.add(Dropout(0.25))
model.add(Dense(2))
model.add(Activation('softmax'))
model.summary()

model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])

epochs = 30
batch_size = 64
img_height, img_width = 150, 150
input_shape = (img_height, img_width, 3)


def create_data(data_base):
    data_base = pathlib.Path(data_base)
    splitfolders.ratio(data_base, output='images/', seed=1234, ratio=(0.7, 0.1, 0.2), group_prefix=None)
    data_generator = ImageDataGenerator(rescale=1.0 / 255)
    train_generator = data_generator.flow_from_directory('images/train/', target_size=(img_height, img_width),
                                                         class_mode='categorical', batch_size=batch_size,
                                                         subset='training')
    valid_generator = data_generator.flow_from_directory('images/val/', target_size=(img_height, img_width),
                                                         class_mode='categorical', batch_size=batch_size, shuffle=False)
    test_generator = data_generator.flow_from_directory('images/test/', target_size=(img_height, img_width),
                                                        class_mode='categorical', batch_size=batch_size, shuffle=False)
    return train_generator, valid_generator, test_generator


train_data, valid_data, test_data = create_data('C:/Users/amanz/PycharmProjects/stage/data/data_augm_clt')
import flwr as fl
class FlowerClient(fl.client.NumPyClient):
    def get_parameters(self):
        return model.get_weights()
    def fit(self,parameters,config):
        model.set_weights(parameters)
        history_1 = model.fit_generator(generator=train_data, epochs=5,validation_data=valid_data)
        print("Fit history : ",history_1.history)
        results = {
            "loss": history_1.history["loss"][0],
            "accuracy": history_1.history["accuracy"][0],
            "val_loss": history_1.history["val_loss"][0],
            "val_accuracy": history_1.history["val_accuracy"][0],
        }
        return model.get_weights(),len(train_data),results
    def evaluate(self,parameters,config):
        model.set_weights(parameters)
        train_loss, train_acc = model.evaluate_generator(generator=test_data, steps=16)
        print('Eval accuracy : ',train_acc)
        return train_loss,len(test_data),{"accuracy":train_acc}

fl.client.start_numpy_client(server_address="localhost:8080",
                             client=FlowerClient(),
                             grpc_max_message_length=1024*1024*1024)