LSTM seq2seq 模型之英语到法语翻译

1. 数据集下载

2. 预处理的完整的代码

import os.path

import numpy as np
import tensorflow as tf
import keras
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.preprocessing.text import Tokenizer
from keras.utils import pad_sequences
from sklearn.model_selection import train_test_split
import pandas as pd
import numpy as np
import json

from nlp.util.matplotlib_helper import *


batch_size = 36  # Batch size for training.
epochs = 50  # Number of epochs to train for.
latent_dim = 256  # Latent dimensionality of the encoding space.
num_samples = 100000  # Number of samples to train on.
# Path to the data txt file on disk.

# base_dir = "dataset"
base_dir = "C:/apps/ml_datasets/fra-eng"

dataset_path = os.path.join(base_dir, "fra.txt")

START_ = "sostok "
END_ = " eostok"

def get_dataset():
    # Vectorize the data.
    input_texts = []
    target_texts = []
    input_characters = set()
    target_characters = set()
    with open(dataset_path, "r", encoding="utf-8") as f:
        lines = f.read().split("n")
        print("length of lines:"+ str(len(lines)))
    for line in lines[: min(num_samples, len(lines) - 1)]:
        input_text, target_text, _ = line.split("t")
        # We use "tab" as the "start sequence" character
        # for the targets, and "n" as "end sequence" character.
        target_text = START_ + target_text + END_
        input_texts.append(input_text)
        target_texts.append(target_text)
        for char in input_text:
            if char not in input_characters:
                input_characters.add(char)
        for char in target_text:
            if char not in target_characters:
                target_characters.add(char)
    input_characters = sorted(list(input_characters))
    target_characters = sorted(list(target_characters))
    num_encoder_tokens = len(input_characters)
    num_decoder_tokens = len(target_characters)
    max_encoder_seq_length = max([len(txt) for txt in input_texts])
    max_decoder_seq_length = max([len(txt) for txt in target_texts])
    print("Number of samples:", len(input_texts))
    print("Number of unique input tokens:", num_encoder_tokens)
    print("Number of unique output tokens:", num_decoder_tokens)
    print("Max sequence length for inputs:", max_encoder_seq_length)
    print("Max sequence length for outputs:", max_decoder_seq_length)

    return input_texts, target_texts


def check_distribution(x_train ,y_train):
    x_count = []
    y_count = []

    for sent in x_train:
        x_count.append(len(sent.split()))
    for sent in y_train:
        y_count.append(len(sent.split()))

    graph_df = pd.DataFrame()
    graph_df['x_count'] = x_count
    graph_df['y_count'] = y_count

    import matplotlib.pyplot as plt

    graph_df.hist(bins = 5)
    plt.show()

    # Check how much % of summary have 0-15 words
    cnt = 0
    for i in x_train:
        if (len(i.split()) <= 6):
            cnt = cnt + 1
    print(cnt / len(x_train))

    # Check how much % of summary have 0-15 words
    cnt = 0
    for i in y_train:
        if (len(i.split()) <= 10):
            cnt = cnt + 1
    print(cnt / len(y_train))



def get_tokenizers(x_train ,y_train):

    input_tokenizer = Tokenizer()
    input_tokenizer.fit_on_texts(x_train)

    target_tokenizer = Tokenizer()
    target_tokenizer.fit_on_texts(y_train)

    input_sequences = input_tokenizer.texts_to_sequences(x_train)
    target_sequences = target_tokenizer.texts_to_sequences(y_train)

    max_text_len = max(len(seq) for seq in input_sequences)
    max_summary_len = max(len(seq) for seq in target_sequences)
    print('max_text_len:', max_text_len)
    print('max_summary_len:', max_summary_len)


    max_text_len = 6
    max_summary_len = 10

    input_sequences = pad_sequences(input_sequences, maxlen=max_text_len, padding='post')
    target_sequences = pad_sequences(target_sequences, maxlen=max_summary_len, padding='post')

    print(max_text_len, input_sequences[0])
    print(max_summary_len, target_sequences[0])


    x_voc = len(input_tokenizer.word_counts)+1
    print("Size of vocabulary in X = {}".format(x_voc))

    y_voc = len(target_tokenizer.word_counts)+1
    print("Size of vocabulary in Y = {}".format(y_voc))

    y_tokenizer_json = target_tokenizer.to_json()

    # Save the tokenizer to a file
    y_tokenizer_file = os.path.join(base_dir, "y_tokenizer.json")
    with open(y_tokenizer_file, 'w', encoding='utf-8') as f:
        f.write(json.dumps(y_tokenizer_json, ensure_ascii=False))


    x_tokenizer_json = input_tokenizer.to_json()

    # Save the tokenizer to a file
    x_tokenizer_file = os.path.join(base_dir, "x_tokenizer.json")
    with open(x_tokenizer_file, 'w', encoding='utf-8') as f:
        f.write(json.dumps(x_tokenizer_json, ensure_ascii=False))


    return input_tokenizer, target_tokenizer, input_sequences, target_sequences


if __name__ == '__main__':

    x_train ,y_train = get_dataset()

    # check_distribution(x_train, y_train)

    input_tokenizer, target_tokenizer, input_sequences, target_sequences = get_tokenizers(x_train ,y_train);

    # Split the dataset into training and validation sets
    x_train, x_val, y_train, y_val = train_test_split( input_sequences, target_sequences, test_size=0.2, random_state=42 )

    np.savez(os.path.join(base_dir,"train"), x_tr=x_train, y_tr=y_train, x_val=x_val, y_val=y_val)

    # for i in range(5):
    #     print("x = ", x_train[i] )
    #     print("y = ", y_train[i])

3. 训练 模型的完整 代码

import keras
from keras.layers import Input, LSTM, Embedding, Dense, Concatenate, TimeDistributed
from keras.models import Model
from keras.callbacks import EarlyStopping
from keras.preprocessing.text import tokenizer_from_json
import json
import numpy as np
import os

model_path = "model"

latent_dim = 200
embedding_dim = 100
max_text_len = 6
max_summary_len = 10

base_dir = "C:/apps/ml_datasets/fra-eng"


START_ = "sostok"
END_ = "eostok"

def get_dataset():

    train_set = np.load(os.path.join(base_dir, "train.npz"))
    x_tr = train_set['x_tr']
    y_tr = train_set['y_tr']
    x_val = train_set['x_val']
    y_val = train_set['y_val']

    print("X_train:", x_tr.shape)
    print("y_train:", y_tr.shape)
    print("X_test:", x_val.shape)
    print("y_test:", y_val.shape)

    with open(os.path.join(base_dir, "y_tokenizer.json")) as f:
        data = json.load(f)
        y_tokenizer = tokenizer_from_json(data)

    with open(os.path.join(base_dir,"x_tokenizer.json")) as f:
        data = json.load(f)
        x_tokenizer = tokenizer_from_json(data)

    return x_tr, y_tr, x_val, y_val, x_tokenizer, y_tokenizer

def get_model( max_text_len = 100, x_voc = 33288, y_voc = 11572):
    # K.clear_session()

    # Encoder
    encoder_inputs = Input(shape=(max_text_len,), name="encoder_inputs")

    #embedding layer
    enc_emb =  Embedding(x_voc, embedding_dim,trainable=True, name= "encoder_embedding")(encoder_inputs)

    #encoder lstm 1
    encoder_lstm1 = LSTM(latent_dim,return_sequences=True,return_state=True,dropout=0.4,recurrent_dropout=0.4, name = "encoder_lstm1")
    # encoder_outputs, state_h, state_c = encoder_lstm1(enc_emb)
    encoder_output1, state_h1, state_c1 = encoder_lstm1(enc_emb)

    #encoder lstm 2
    encoder_lstm2 = LSTM(latent_dim,return_sequences=True,return_state=True,dropout=0.4,recurrent_dropout=0.4, name = "encoder_lstm2")
    encoder_output2, state_h2, state_c2 = encoder_lstm2(encoder_output1)

    #encoder lstm 3
    encoder_lstm3=LSTM(latent_dim, return_state=True, return_sequences=True,dropout=0.4,recurrent_dropout=0.4, name = "encoder_lstm3")
    encoder_outputs, state_h, state_c= encoder_lstm3(encoder_output2)

    # Set up the decoder, using `encoder_states` as initial state.
    decoder_inputs = Input(shape=(None,), name="decoder_inputs")

    #embedding layer
    dec_emb_layer = Embedding(y_voc, embedding_dim, trainable=True, name="decoder_embedding")
    dec_emb = dec_emb_layer(decoder_inputs)

    decoder_lstm = LSTM(latent_dim, return_sequences=True, return_state=True,dropout=0.4,recurrent_dropout=0.2, name="decoder_lstm")
    decoder_outputs,decoder_fwd_state, decoder_back_state = decoder_lstm(dec_emb,initial_state=[state_h, state_c])

    #dense layer
    decoder_dense =  Dense(y_voc, activation='softmax', name="decoder_outputs")
    decoder_outputs = decoder_dense(decoder_outputs)

    # Define the model
    model = Model([encoder_inputs, decoder_inputs], decoder_outputs)


    model.summary()

    model.compile(optimizer='rmsprop', loss='sparse_categorical_crossentropy', metrics=["accuracy"])
    # model.compile(optimizer=keras.optimizers.Adam(), loss=keras.losses.SparseCategoricalCrossentropy())

    # model = keras.models.load_model('model.h5')

    return model


def train(model, x_tr,y_tr, x_val, y_val, epochs=50):
    # model, encoder_model, decoder_model, x_tokenizer, y_tokenizer =  load_model()

    x_train_input = x_tr
    y_train_input = y_tr[:, :-1]
    y_train_output = y_tr.reshape(y_tr.shape[0], y_tr.shape[1], 1)[:, 1:]

    print('y_train_input[0]', y_train_input[0])
    print('y_train_output[0]', y_train_output[0])

    x_val_input = x_val
    y_val_input = y_val[:, :-1]
    y_val_output = y_val.reshape(y_val.shape[0], y_val.shape[1], 1)[:, 1:]

    early_stop = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=2)
    checkpoint = keras.callbacks.ModelCheckpoint(model_path, monitor='val_loss', save_best_only=True, mode='min', verbose=1)

    callbacks = [ early_stop, checkpoint]

    history = model.fit([x_train_input, y_train_input], y_train_output, epochs=epochs,
                        callbacks=callbacks, batch_size=128, validation_data=([x_val_input, y_val_input], y_val_output))

    model.save(model_path)

    return history

def get_encoder_decoder_model1(model):

    for i, layer in enumerate(model.layers):
        print(f"Layer {i}: {layer.name} - {layer.__class__.__name__}")

    decoder_inputs = model.get_layer("decoder_inputs").input
    encoder_inputs = model.get_layer("encoder_inputs").input

    encoder_outputs, state_h_enc, state_c_enc = model.get_layer("encoder_lstm3").output  # Change index if needed
    decoder_lstm = model.get_layer("decoder_lstm")  # Change index if needed
    decoder_dense = model.get_layer("decoder_outputs") # Change index if needed

    encoder_model = Model(inputs=encoder_inputs, outputs=[encoder_outputs, state_h_enc, state_c_enc])

    decoder_state_input_h = Input(shape=(latent_dim,))
    decoder_state_input_c = Input(shape=(latent_dim,))
    dec_emb2 = model.get_layer("decoder_embedding")(decoder_inputs) # Assuming embedding layer is at index 3, change if needed

    decoder_outputs2, state_h2, state_c2 = decoder_lstm( dec_emb2, initial_state=[decoder_state_input_h, decoder_state_input_c] )

    decoder_outputs2 = decoder_dense(decoder_outputs2)

    decoder_model = Model(
        [decoder_inputs] + [decoder_state_input_h, decoder_state_input_c],
        [decoder_outputs2] + [state_h2, state_c2]
    )



    return decoder_model, encoder_model

def load_model():
    model = keras.models.load_model(model_path)
    decoder_model, encoder_model = get_encoder_decoder_model1(model)

    with open(os.path.join(base_dir, "y_tokenizer.json")) as f:
        data = json.load(f)
        y_tokenizer = tokenizer_from_json(data)

    with open(os.path.join(base_dir, "x_tokenizer.json")) as f:
        data = json.load(f)
        x_tokenizer = tokenizer_from_json(data)

    return model , encoder_model , decoder_model, x_tokenizer, y_tokenizer

def decode_sequence(input_seq, encoder_model, decoder_model,  target_word_index, reverse_target_word_index):

    # Encode the input as state vectors.
    _, e_h, e_c = encoder_model.predict(input_seq)

    # Generate empty target sequence of length 1.
    target_seq = np.zeros((1, 1))

    # Populate the first word of target sequence with the start word.
    target_seq[0, 0] = target_word_index[START_]

    stop_condition = False
    decoded_sentence = ''
    while not stop_condition:

        output_tokens, h, c = decoder_model.predict([target_seq] + [e_h, e_c])

        # Sample a token
        sampled_token_index = np.argmax(output_tokens[0, -1, :])
        print(sampled_token_index)
        if sampled_token_index ==0 :
            sampled_token='0'
        else:
            sampled_token = reverse_target_word_index[sampled_token_index]


        if (sampled_token != END_):
            decoded_sentence += ' ' + sampled_token

        # Exit condition: either hit max length or find stop word.
        if (sampled_token == END_ or len(decoded_sentence.split()) >= (max_summary_len - 1)):
            stop_condition = True

        # Update the target sequence (of length 1).
        target_seq = np.zeros((1, 1))
        target_seq[0, 0] = sampled_token_index

        # Update internal states
        e_h, e_c = h, c

    return decoded_sentence

def seq2summary(input_seq, target_word_index, reverse_target_word_index):
    newString=''
    for i in input_seq:
        if((i!=0 and i!=target_word_index[START_]) and i!=target_word_index[END_]):
            newString=newString+reverse_target_word_index[i]+' '
    return newString

def seq2text(input_seq, reverse_source_word_index):
    newString=''
    for i in input_seq:
        if(i!=0):
            newString=newString+reverse_source_word_index[i]+' '
    return newString


def show_matrix(history):
    # from matplotlib import pyplot
    # pyplot.plot(history.history['loss'], label='train')
    # pyplot.plot(history.history['val_loss'], label='test')
    # pyplot.legend()
    # pyplot.show()
    pass

def test(max_text_len = 100):
    x_tr, y_tr, x_val, y_val, x_tokenizer, y_tokenizer =  get_dataset()
    model , encoder_model , decoder_model, x_tokenizer, y_tokenizer = load_model()
    reverse_target_word_index = y_tokenizer.index_word
    reverse_source_word_index = x_tokenizer.index_word
    target_word_index = y_tokenizer.word_index

    for i in range(5, 50):
        print("Review:", seq2text(x_tr[i], reverse_source_word_index))
        print("Original summary:", seq2summary(y_tr[i], target_word_index, reverse_target_word_index))
        print("Predicted summary:",
              decode_sequence(x_tr[i].reshape(1, max_text_len), encoder_model, decoder_model, target_word_index,
                              reverse_target_word_index))
        print("n")


if __name__ == '__main__':

    x_voc = 8864
    y_voc = 19131
    epochs = 100

    x_tr, y_tr, x_val, y_val, x_tokenizer, y_tokenizer = get_dataset()

    print(x_tr[0])
    print(y_tr[0])

    model = get_model(max_text_len, x_voc, y_voc)
    history = train(model, x_tr, y_tr, x_val, y_val, epochs)
    show_matrix(history)
    test(max_text_len)

4.训练 过程中数据

cpu 上训练时间大概是一整天

X_train: (80000, 6)
y_train: (80000, 10)
X_test: (20000, 6)
y_test: (20000, 10)
[ 30 266  29  12 178   0]
[  1  12  33 355  62  74 112   2   0   0]
Model: "model"
__________________________________________________________________________________________________
 Layer (type)                   Output Shape         Param #     Connected to
==================================================================================================
 encoder_inputs (InputLayer)    [(None, 6)]          0           []

 encoder_embedding (Embedding)  (None, 6, 100)       886400      ['encoder_inputs[0][0]']

 encoder_lstm1 (LSTM)           [(None, 6, 200),     240800      ['encoder_embedding[0][0]']
                                 (None, 200),
                                 (None, 200)]

 decoder_inputs (InputLayer)    [(None, None)]       0           []

 encoder_lstm2 (LSTM)           [(None, 6, 200),     320800      ['encoder_lstm1[0][0]']
                                 (None, 200),
                                 (None, 200)]

 decoder_embedding (Embedding)  (None, None, 100)    1913100     ['decoder_inputs[0][0]']

 encoder_lstm3 (LSTM)           [(None, 6, 200),     320800      ['encoder_lstm2[0][0]']
                                 (None, 200),
                                 (None, 200)]

 decoder_lstm (LSTM)            [(None, None, 200),  240800      ['decoder_embedding[0][0]',
                                 (None, 200),                     'encoder_lstm3[0][1]',
                                 (None, 200)]                     'encoder_lstm3[0][2]']

 decoder_outputs (Dense)        (None, None, 19131)  3845331     ['decoder_lstm[0][0]']

==================================================================================================
Total params: 7,768,031
Trainable params: 7,768,031
Non-trainable params: 0
__________________________________________________________________________________________________
y_train_input[0] [  1  12  33 355  62  74 112   2   0]
y_train_output[0] [[ 12]
 [ 33]
 [355]
 [ 62]
 [ 74]
 [112]
 [  2]
 [  0]
 [  0]]
Epoch 1/100
625/625 [==============================] - ETA: 0s - loss: 3.8344 - accuracy: 0.4682
Epoch 1: val_loss improved from inf to 3.35198, saving model to model

625/625 [==============================] - 523s 824ms/step - loss: 3.8344 - accuracy: 0.4682 - val_loss: 3.3520 - val_accuracy: 0.5184
Epoch 2/100
625/625 [==============================] - ETA: 0s - loss: 3.2284 - accuracy: 0.5280
Epoch 2: val_loss improved from 3.35198 to 3.08983, saving model to model

625/625 [==============================] - 622s 996ms/step - loss: 3.2284 - accuracy: 0.5280 - val_loss: 3.0898 - val_accuracy: 0.5403
Epoch 3/100
625/625 [==============================] - ETA: 0s - loss: 3.0036 - accuracy: 0.5493
Epoch 3: val_loss improved from 3.08983 to 2.89494, saving model to model

625/625 [==============================] - 753s 1s/step - loss: 3.0036 - accuracy: 0.5493 - val_loss: 2.8949 - val_accuracy: 0.5637
Epoch 4/100
625/625 [==============================] - ETA: 0s - loss: 2.8115 - accuracy: 0.5733
Epoch 4: val_loss improved from 2.89494 to 2.70395, saving model to model

625/625 [==============================] - 1114s 2s/step - loss: 2.8115 - accuracy: 0.5733 - val_loss: 2.7039 - val_accuracy: 0.5897
Epoch 5/100
625/625 [==============================] - ETA: 0s - loss: 2.6470 - accuracy: 0.5930
Epoch 5: val_loss improved from 2.70395 to 2.57205, saving model to model

625/625 [==============================] - 1075s 2s/step - loss: 2.6470 - accuracy: 0.5930 - val_loss: 2.5721 - val_accuracy: 0.6046
Epoch 6/100
625/625 [==============================] - ETA: 0s - loss: 2.5275 - accuracy: 0.6063
Epoch 6: val_loss improved from 2.57205 to 2.47006, saving model to model

625/625 [==============================] - 1289s 2s/step - loss: 2.5275 - accuracy: 0.6063 - val_loss: 2.4701 - val_accuracy: 0.6164
Epoch 7/100
625/625 [==============================] - ETA: 0s - loss: 2.4297 - accuracy: 0.6172
Epoch 7: val_loss improved from 2.47006 to 2.37810, saving model to model

625/625 [==============================] - 1403s 2s/step - loss: 2.4297 - accuracy: 0.6172 - val_loss: 2.3781 - val_accuracy: 0.6273
Epoch 8/100
625/625 [==============================] - ETA: 0s - loss: 2.3421 - accuracy: 0.6264
Epoch 8: val_loss improved from 2.37810 to 2.30006, saving model to model

625/625 [==============================] - 1420s 2s/step - loss: 2.3421 - accuracy: 0.6264 - val_loss: 2.3001 - val_accuracy: 0.6363
Epoch 9/100
625/625 [==============================] - ETA: 0s - loss: 2.2645 - accuracy: 0.6353
Epoch 9: val_loss improved from 2.30006 to 2.23482, saving model to model

625/625 [==============================] - 1315s 2s/step - loss: 2.2645 - accuracy: 0.6353 - val_loss: 2.2348 - val_accuracy: 0.6437
Epoch 10/100
625/625 [==============================] - ETA: 0s - loss: 2.1948 - accuracy: 0.6420
Epoch 10: val_loss improved from 2.23482 to 2.17029, saving model to model

625/625 [==============================] - 1338s 2s/step - loss: 2.1948 - accuracy: 0.6420 - val_loss: 2.1703 - val_accuracy: 0.6510
Epoch 11/100
625/625 [==============================] - ETA: 0s - loss: 2.1300 - accuracy: 0.6484
Epoch 11: val_loss improved from 2.17029 to 2.11222, saving model to model

625/625 [==============================] - 1345s 2s/step - loss: 2.1300 - accuracy: 0.6484 - val_loss: 2.1122 - val_accuracy: 0.6572
Epoch 12/100
625/625 [==============================] - ETA: 0s - loss: 2.0684 - accuracy: 0.6553
Epoch 12: val_loss improved from 2.11222 to 2.06084, saving model to model

625/625 [==============================] - 1429s 2s/step - loss: 2.0684 - accuracy: 0.6553 - val_loss: 2.0608 - val_accuracy: 0.6621
Epoch 13/100
625/625 [==============================] - ETA: 0s - loss: 2.0104 - accuracy: 0.6612
Epoch 13: val_loss improved from 2.06084 to 2.00748, saving model to model

625/625 [==============================] - 1236s 2s/step - loss: 2.0104 - accuracy: 0.6612 - val_loss: 2.0075 - val_accuracy: 0.6691
Epoch 14/100
625/625 [==============================] - ETA: 0s - loss: 1.9545 - accuracy: 0.6669
Epoch 14: val_loss improved from 2.00748 to 1.96069, saving model to model

625/625 [==============================] - 1337s 2s/step - loss: 1.9545 - accuracy: 0.6669 - val_loss: 1.9607 - val_accuracy: 0.6737
Epoch 15/100
625/625 [==============================] - ETA: 0s - loss: 1.9030 - accuracy: 0.6721
Epoch 15: val_loss improved from 1.96069 to 1.91793, saving model to model

625/625 [==============================] - 1329s 2s/step - loss: 1.9030 - accuracy: 0.6721 - val_loss: 1.9179 - val_accuracy: 0.6784
Epoch 16/100
625/625 [==============================] - ETA: 0s - loss: 1.8557 - accuracy: 0.6769
Epoch 16: val_loss improved from 1.91793 to 1.87738, saving model to model

625/625 [==============================] - 1336s 2s/step - loss: 1.8557 - accuracy: 0.6769 - val_loss: 1.8774 - val_accuracy: 0.6831
Epoch 17/100
625/625 [==============================] - ETA: 0s - loss: 1.8106 - accuracy: 0.6820
Epoch 17: val_loss improved from 1.87738 to 1.83981, saving model to model

625/625 [==============================] - 1482s 2s/step - loss: 1.8106 - accuracy: 0.6820 - val_loss: 1.8398 - val_accuracy: 0.6875
Epoch 18/100
625/625 [==============================] - ETA: 0s - loss: 1.7678 - accuracy: 0.6865
Epoch 18: val_loss improved from 1.83981 to 1.80432, saving model to model

625/625 [==============================] - 1408s 2s/step - loss: 1.7678 - accuracy: 0.6865 - val_loss: 1.8043 - val_accuracy: 0.6913
Epoch 19/100
625/625 [==============================] - ETA: 0s - loss: 1.7265 - accuracy: 0.6909
Epoch 19: val_loss improved from 1.80432 to 1.77152, saving model to model

625/625 [==============================] - 1336s 2s/step - loss: 1.7265 - accuracy: 0.6909 - val_loss: 1.7715 - val_accuracy: 0.6958
Epoch 20/100
625/625 [==============================] - ETA: 0s - loss: 1.6872 - accuracy: 0.6953
Epoch 20: val_loss improved from 1.77152 to 1.73842, saving model to model

625/625 [==============================] - 1386s 2s/step - loss: 1.6872 - accuracy: 0.6953 - val_loss: 1.7384 - val_accuracy: 0.7000
Epoch 21/100
625/625 [==============================] - ETA: 0s - loss: 1.6492 - accuracy: 0.6996
Epoch 21: val_loss improved from 1.73842 to 1.70509, saving model to model

625/625 [==============================] - 1506s 2s/step - loss: 1.6492 - accuracy: 0.6996 - val_loss: 1.7051 - val_accuracy: 0.7050
Epoch 22/100
625/625 [==============================] - ETA: 0s - loss: 1.6125 - accuracy: 0.7039
Epoch 22: val_loss improved from 1.70509 to 1.67770, saving model to model

625/625 [==============================] - 1492s 2s/step - loss: 1.6125 - accuracy: 0.7039 - val_loss: 1.6777 - val_accuracy: 0.7076
Epoch 23/100
625/625 [==============================] - ETA: 0s - loss: 1.5773 - accuracy: 0.7079
Epoch 23: val_loss improved from 1.67770 to 1.64832, saving model to model

625/625 [==============================] - 1471s 2s/step - loss: 1.5773 - accuracy: 0.7079 - val_loss: 1.6483 - val_accuracy: 0.7108
Epoch 24/100
625/625 [==============================] - ETA: 0s - loss: 1.5442 - accuracy: 0.7121
Epoch 24: val_loss improved from 1.64832 to 1.61894, saving model to model

625/625 [==============================] - 1556s 2s/step - loss: 1.5442 - accuracy: 0.7121 - val_loss: 1.6189 - val_accuracy: 0.7145
Epoch 25/100
625/625 [==============================] - ETA: 0s - loss: 1.5112 - accuracy: 0.7160
Epoch 25: val_loss improved from 1.61894 to 1.59627, saving model to model

625/625 [==============================] - 1467s 2s/step - loss: 1.5112 - accuracy: 0.7160 - val_loss: 1.5963 - val_accuracy: 0.7171
Epoch 26/100
625/625 [==============================] - ETA: 0s - loss: 1.4804 - accuracy: 0.7197
Epoch 26: val_loss improved from 1.59627 to 1.56844, saving model to model

625/625 [==============================] - 1594s 3s/step - loss: 1.4804 - accuracy: 0.7197 - val_loss: 1.5684 - val_accuracy: 0.7220
Epoch 27/100
625/625 [==============================] - ETA: 0s - loss: 1.4507 - accuracy: 0.7234
Epoch 27: val_loss improved from 1.56844 to 1.54407, saving model to model

625/625 [==============================] - 1550s 2s/step - loss: 1.4507 - accuracy: 0.7234 - val_loss: 1.5441 - val_accuracy: 0.7252
Epoch 28/100
625/625 [==============================] - ETA: 0s - loss: 1.4213 - accuracy: 0.7268
Epoch 28: val_loss improved from 1.54407 to 1.52123, saving model to model

625/625 [==============================] - 1529s 2s/step - loss: 1.4213 - accuracy: 0.7268 - val_loss: 1.5212 - val_accuracy: 0.7284
Epoch 29/100
625/625 [==============================] - ETA: 0s - loss: 1.3941 - accuracy: 0.7302
Epoch 29: val_loss improved from 1.52123 to 1.50195, saving model to model

625/625 [==============================] - 1482s 2s/step - loss: 1.3941 - accuracy: 0.7302 - val_loss: 1.5020 - val_accuracy: 0.7308
Epoch 30/100
625/625 [==============================] - ETA: 0s - loss: 1.3673 - accuracy: 0.7337
Epoch 30: val_loss improved from 1.50195 to 1.48183, saving model to model

625/625 [==============================] - 1683s 3s/step - loss: 1.3673 - accuracy: 0.7337 - val_loss: 1.4818 - val_accuracy: 0.7339
Epoch 31/100
625/625 [==============================] - ETA: 0s - loss: 1.3423 - accuracy: 0.7369
Epoch 31: val_loss improved from 1.48183 to 1.46150, saving model to model

625/625 [==============================] - 1621s 3s/step - loss: 1.3423 - accuracy: 0.7369 - val_loss: 1.4615 - val_accuracy: 0.7364
Epoch 32/100
625/625 [==============================] - ETA: 0s - loss: 1.3177 - accuracy: 0.7395
Epoch 32: val_loss improved from 1.46150 to 1.44290, saving model to model

625/625 [==============================] - 1594s 3s/step - loss: 1.3177 - accuracy: 0.7395 - val_loss: 1.4429 - val_accuracy: 0.7392
Epoch 33/100
625/625 [==============================] - ETA: 0s - loss: 1.2939 - accuracy: 0.7428
Epoch 33: val_loss improved from 1.44290 to 1.42519, saving model to model

625/625 [==============================] - 1564s 3s/step - loss: 1.2939 - accuracy: 0.7428 - val_loss: 1.4252 - val_accuracy: 0.7408
Epoch 34/100
625/625 [==============================] - ETA: 0s - loss: 1.2713 - accuracy: 0.7454
Epoch 34: val_loss improved from 1.42519 to 1.40973, saving model to model

625/625 [==============================] - 1410s 2s/step - loss: 1.2713 - accuracy: 0.7454 - val_loss: 1.4097 - val_accuracy: 0.7436
Epoch 35/100
625/625 [==============================] - ETA: 0s - loss: 1.2492 - accuracy: 0.7486
Epoch 35: val_loss improved from 1.40973 to 1.39085, saving model to model

625/625 [==============================] - 1407s 2s/step - loss: 1.2492 - accuracy: 0.7486 - val_loss: 1.3909 - val_accuracy: 0.7464
Epoch 36/100
625/625 [==============================] - ETA: 0s - loss: 1.2286 - accuracy: 0.7511
Epoch 36: val_loss improved from 1.39085 to 1.37574, saving model to model

625/625 [==============================] - 1470s 2s/step - loss: 1.2286 - accuracy: 0.7511 - val_loss: 1.3757 - val_accuracy: 0.7485
Epoch 37/100
625/625 [==============================] - ETA: 0s - loss: 1.2086 - accuracy: 0.7537
Epoch 37: val_loss improved from 1.37574 to 1.36135, saving model to model

625/625 [==============================] - 1561s 2s/step - loss: 1.2086 - accuracy: 0.7537 - val_loss: 1.3613 - val_accuracy: 0.7503
Epoch 38/100
625/625 [==============================] - ETA: 0s - loss: 1.1886 - accuracy: 0.7560
Epoch 38: val_loss improved from 1.36135 to 1.34700, saving model to model

625/625 [==============================] - 1723s 3s/step - loss: 1.1886 - accuracy: 0.7560 - val_loss: 1.3470 - val_accuracy: 0.7519
Epoch 39/100
625/625 [==============================] - ETA: 0s - loss: 1.1690 - accuracy: 0.7591
Epoch 39: val_loss improved from 1.34700 to 1.33595, saving model to model

625/625 [==============================] - 1796s 3s/step - loss: 1.1690 - accuracy: 0.7591 - val_loss: 1.3360 - val_accuracy: 0.7536
Epoch 40/100
625/625 [==============================] - ETA: 0s - loss: 1.1516 - accuracy: 0.7611
Epoch 40: val_loss improved from 1.33595 to 1.32075, saving model to model

625/625 [==============================] - 1883s 3s/step - loss: 1.1516 - accuracy: 0.7611 - val_loss: 1.3208 - val_accuracy: 0.7567
Epoch 41/100
625/625 [==============================] - ETA: 0s - loss: 1.1329 - accuracy: 0.7641
Epoch 41: val_loss improved from 1.32075 to 1.30833, saving model to model

625/625 [==============================] - 1924s 3s/step - loss: 1.1329 - accuracy: 0.7641 - val_loss: 1.3083 - val_accuracy: 0.7582
Epoch 42/100
625/625 [==============================] - ETA: 0s - loss: 1.1154 - accuracy: 0.7661
Epoch 42: val_loss improved from 1.30833 to 1.29805, saving model to model

625/625 [==============================] - 1726s 3s/step - loss: 1.1154 - accuracy: 0.7661 - val_loss: 1.2980 - val_accuracy: 0.7590
Epoch 43/100
625/625 [==============================] - ETA: 0s - loss: 1.0988 - accuracy: 0.7688
Epoch 43: val_loss improved from 1.29805 to 1.28425, saving model to model

625/625 [==============================] - 1588s 3s/step - loss: 1.0988 - accuracy: 0.7688 - val_loss: 1.2843 - val_accuracy: 0.7619
Epoch 44/100
625/625 [==============================] - ETA: 0s - loss: 1.0836 - accuracy: 0.7700
Epoch 44: val_loss improved from 1.28425 to 1.27422, saving model to model

625/625 [==============================] - 1559s 2s/step - loss: 1.0836 - accuracy: 0.7700 - val_loss: 1.2742 - val_accuracy: 0.7623
Epoch 45/100
625/625 [==============================] - ETA: 0s - loss: 1.0658 - accuracy: 0.7731
Epoch 45: val_loss improved from 1.27422 to 1.26191, saving model to model

625/625 [==============================] - 1663s 3s/step - loss: 1.0658 - accuracy: 0.7731 - val_loss: 1.2619 - val_accuracy: 0.7648
Epoch 46/100
625/625 [==============================] - ETA: 0s - loss: 1.0521 - accuracy: 0.7748
Epoch 46: val_loss improved from 1.26191 to 1.25429, saving model to model

625/625 [==============================] - 1829s 3s/step - loss: 1.0521 - accuracy: 0.7748 - val_loss: 1.2543 - val_accuracy: 0.7654
Epoch 47/100
625/625 [==============================] - ETA: 0s - loss: 1.0367 - accuracy: 0.7768
Epoch 47: val_loss improved from 1.25429 to 1.24540, saving model to model

625/625 [==============================] - 1825s 3s/step - loss: 1.0367 - accuracy: 0.7768 - val_loss: 1.2454 - val_accuracy: 0.7665
Epoch 48/100
625/625 [==============================] - ETA: 0s - loss: 1.0228 - accuracy: 0.7788
Epoch 48: val_loss improved from 1.24540 to 1.23448, saving model to model

625/625 [==============================] - 1875s 3s/step - loss: 1.0228 - accuracy: 0.7788 - val_loss: 1.2345 - val_accuracy: 0.7683
Epoch 49/100
625/625 [==============================] - ETA: 0s - loss: 1.0085 - accuracy: 0.7808
Epoch 49: val_loss improved from 1.23448 to 1.22543, saving model to model

625/625 [==============================] - 1919s 3s/step - loss: 1.0085 - accuracy: 0.7808 - val_loss: 1.2254 - val_accuracy: 0.7696
Epoch 50/100
625/625 [==============================] - ETA: 0s - loss: 0.9947 - accuracy: 0.7825
Epoch 50: val_loss improved from 1.22543 to 1.21777, saving model to model

625/625 [==============================] - 1754s 3s/step - loss: 0.9947 - accuracy: 0.7825 - val_loss: 1.2178 - val_accuracy: 0.7700
Epoch 51/100
625/625 [==============================] - ETA: 0s - loss: 0.9813 - accuracy: 0.7848
Epoch 51: val_loss improved from 1.21777 to 1.20699, saving model to model

625/625 [==============================] - 1753s 3s/step - loss: 0.9813 - accuracy: 0.7848 - val_loss: 1.2070 - val_accuracy: 0.7726
Epoch 52/100
625/625 [==============================] - ETA: 0s - loss: 0.9683 - accuracy: 0.7867
Epoch 52: val_loss improved from 1.20699 to 1.19979, saving model to model

625/625 [==============================] - 1705s 3s/step - loss: 0.9683 - accuracy: 0.7867 - val_loss: 1.1998 - val_accuracy: 0.7730
Epoch 53/100
625/625 [==============================] - ETA: 0s - loss: 0.9562 - accuracy: 0.7881
Epoch 53: val_loss improved from 1.19979 to 1.19423, saving model to model

625/625 [==============================] - 1669s 3s/step - loss: 0.9562 - accuracy: 0.7881 - val_loss: 1.1942 - val_accuracy: 0.7740
Epoch 54/100
625/625 [==============================] - ETA: 0s - loss: 0.9433 - accuracy: 0.7902
Epoch 54: val_loss improved from 1.19423 to 1.18470, saving model to model

625/625 [==============================] - 1731s 3s/step - loss: 0.9433 - accuracy: 0.7902 - val_loss: 1.1847 - val_accuracy: 0.7759
Epoch 55/100
625/625 [==============================] - ETA: 0s - loss: 0.9318 - accuracy: 0.7916
Epoch 55: val_loss improved from 1.18470 to 1.17739, saving model to model

625/625 [==============================] - 1711s 3s/step - loss: 0.9318 - accuracy: 0.7916 - val_loss: 1.1774 - val_accuracy: 0.7764
Epoch 56/100
625/625 [==============================] - ETA: 0s - loss: 0.9212 - accuracy: 0.7933
Epoch 56: val_loss improved from 1.17739 to 1.17282, saving model to model

625/625 [==============================] - 1694s 3s/step - loss: 0.9212 - accuracy: 0.7933 - val_loss: 1.1728 - val_accuracy: 0.7773
Epoch 57/100
625/625 [==============================] - ETA: 0s - loss: 0.9108 - accuracy: 0.7944
Epoch 57: val_loss improved from 1.17282 to 1.16557, saving model to model

625/625 [==============================] - 1747s 3s/step - loss: 0.9108 - accuracy: 0.7944 - val_loss: 1.1656 - val_accuracy: 0.7786
Epoch 58/100
625/625 [==============================] - ETA: 0s - loss: 0.8990 - accuracy: 0.7966
Epoch 58: val_loss improved from 1.16557 to 1.16054, saving model to model

625/625 [==============================] - 1629s 3s/step - loss: 0.8990 - accuracy: 0.7966 - val_loss: 1.1605 - val_accuracy: 0.7795
Epoch 59/100
625/625 [==============================] - ETA: 0s - loss: 0.8879 - accuracy: 0.7978
Epoch 59: val_loss improved from 1.16054 to 1.15364, saving model to model

625/625 [==============================] - 1554s 2s/step - loss: 0.8879 - accuracy: 0.7978 - val_loss: 1.1536 - val_accuracy: 0.7797
Epoch 60/100
625/625 [==============================] - ETA: 0s - loss: 0.8777 - accuracy: 0.7996
Epoch 60: val_loss improved from 1.15364 to 1.14653, saving model to model

625/625 [==============================] - 1463s 2s/step - loss: 0.8777 - accuracy: 0.7996 - val_loss: 1.1465 - val_accuracy: 0.7813
Epoch 61/100
625/625 [==============================] - ETA: 0s - loss: 0.8680 - accuracy: 0.8008
Epoch 61: val_loss improved from 1.14653 to 1.14013, saving model to model

625/625 [==============================] - 1493s 2s/step - loss: 0.8680 - accuracy: 0.8008 - val_loss: 1.1401 - val_accuracy: 0.7822
Epoch 62/100
625/625 [==============================] - ETA: 0s - loss: 0.8581 - accuracy: 0.8025
Epoch 62: val_loss improved from 1.14013 to 1.13445, saving model to model

625/625 [==============================] - 1566s 3s/step - loss: 0.8581 - accuracy: 0.8025 - val_loss: 1.1345 - val_accuracy: 0.7835
Epoch 63/100
625/625 [==============================] - ETA: 0s - loss: 0.8482 - accuracy: 0.8041
Epoch 63: val_loss improved from 1.13445 to 1.13095, saving model to model

625/625 [==============================] - 1547s 2s/step - loss: 0.8482 - accuracy: 0.8041 - val_loss: 1.1310 - val_accuracy: 0.7836
Epoch 64/100
625/625 [==============================] - ETA: 0s - loss: 0.8392 - accuracy: 0.8055
Epoch 64: val_loss improved from 1.13095 to 1.12212, saving model to model

625/625 [==============================] - 1585s 3s/step - loss: 0.8392 - accuracy: 0.8055 - val_loss: 1.1221 - val_accuracy: 0.7854
Epoch 65/100
625/625 [==============================] - ETA: 0s - loss: 0.8291 - accuracy: 0.8071
Epoch 65: val_loss improved from 1.12212 to 1.11893, saving model to model

625/625 [==============================] - 1555s 2s/step - loss: 0.8291 - accuracy: 0.8071 - val_loss: 1.1189 - val_accuracy: 0.7851
Epoch 66/100
625/625 [==============================] - ETA: 0s - loss: 0.8204 - accuracy: 0.8083
Epoch 66: val_loss improved from 1.11893 to 1.11534, saving model to model

625/625 [==============================] - 1553s 2s/step - loss: 0.8204 - accuracy: 0.8083 - val_loss: 1.1153 - val_accuracy: 0.7855
Epoch 67/100
625/625 [==============================] - ETA: 0s - loss: 0.8119 - accuracy: 0.8095
Epoch 67: val_loss improved from 1.11534 to 1.11159, saving model to model

625/625 [==============================] - 1587s 3s/step - loss: 0.8119 - accuracy: 0.8095 - val_loss: 1.1116 - val_accuracy: 0.7863
Epoch 68/100
625/625 [==============================] - ETA: 0s - loss: 0.8035 - accuracy: 0.8109
Epoch 68: val_loss improved from 1.11159 to 1.10478, saving model to model

625/625 [==============================] - 1679s 3s/step - loss: 0.8035 - accuracy: 0.8109 - val_loss: 1.1048 - val_accuracy: 0.7874
Epoch 69/100
625/625 [==============================] - ETA: 0s - loss: 0.7956 - accuracy: 0.8119
Epoch 69: val_loss improved from 1.10478 to 1.10363, saving model to model

625/625 [==============================] - 1656s 3s/step - loss: 0.7956 - accuracy: 0.8119 - val_loss: 1.1036 - val_accuracy: 0.7878
Epoch 70/100
625/625 [==============================] - ETA: 0s - loss: 0.7869 - accuracy: 0.8134
Epoch 70: val_loss improved from 1.10363 to 1.09615, saving model to model

625/625 [==============================] - 1660s 3s/step - loss: 0.7869 - accuracy: 0.8134 - val_loss: 1.0962 - val_accuracy: 0.7889
Epoch 71/100
625/625 [==============================] - ETA: 0s - loss: 0.7790 - accuracy: 0.8148
Epoch 71: val_loss improved from 1.09615 to 1.09203, saving model to model

625/625 [==============================] - 1667s 3s/step - loss: 0.7790 - accuracy: 0.8148 - val_loss: 1.0920 - val_accuracy: 0.7894
Epoch 72/100
625/625 [==============================] - ETA: 0s - loss: 0.7707 - accuracy: 0.8164
Epoch 72: val_loss improved from 1.09203 to 1.08892, saving model to model

625/625 [==============================] - 1664s 3s/step - loss: 0.7707 - accuracy: 0.8164 - val_loss: 1.0889 - val_accuracy: 0.7903
Epoch 73/100
625/625 [==============================] - ETA: 0s - loss: 0.7640 - accuracy: 0.8170
Epoch 73: val_loss improved from 1.08892 to 1.08577, saving model to model

625/625 [==============================] - 1701s 3s/step - loss: 0.7640 - accuracy: 0.8170 - val_loss: 1.0858 - val_accuracy: 0.7909
Epoch 74/100
625/625 [==============================] - ETA: 0s - loss: 0.7567 - accuracy: 0.8182
Epoch 74: val_loss improved from 1.08577 to 1.08194, saving model to model

625/625 [==============================] - 1632s 3s/step - loss: 0.7567 - accuracy: 0.8182 - val_loss: 1.0819 - val_accuracy: 0.7915
Epoch 75/100
625/625 [==============================] - ETA: 0s - loss: 0.7482 - accuracy: 0.8195
Epoch 75: val_loss improved from 1.08194 to 1.07837, saving model to model

625/625 [==============================] - 1812s 3s/step - loss: 0.7482 - accuracy: 0.8195 - val_loss: 1.0784 - val_accuracy: 0.7919
Epoch 76/100
625/625 [==============================] - ETA: 0s - loss: 0.7413 - accuracy: 0.8208
Epoch 76: val_loss improved from 1.07837 to 1.07431, saving model to model

625/625 [==============================] - 1680s 3s/step - loss: 0.7413 - accuracy: 0.8208 - val_loss: 1.0743 - val_accuracy: 0.7919
Epoch 77/100
625/625 [==============================] - ETA: 0s - loss: 0.7348 - accuracy: 0.8217
Epoch 77: val_loss improved from 1.07431 to 1.07171, saving model to model

625/625 [==============================] - 1841s 3s/step - loss: 0.7348 - accuracy: 0.8217 - val_loss: 1.0717 - val_accuracy: 0.7922
Epoch 78/100
625/625 [==============================] - ETA: 0s - loss: 0.7276 - accuracy: 0.8231
Epoch 78: val_loss improved from 1.07171 to 1.06717, saving model to model

625/625 [==============================] - 1713s 3s/step - loss: 0.7276 - accuracy: 0.8231 - val_loss: 1.0672 - val_accuracy: 0.7936
Epoch 79/100
625/625 [==============================] - ETA: 0s - loss: 0.7214 - accuracy: 0.8237
Epoch 79: val_loss improved from 1.06717 to 1.06485, saving model to model

625/625 [==============================] - 1678s 3s/step - loss: 0.7214 - accuracy: 0.8237 - val_loss: 1.0648 - val_accuracy: 0.7945
Epoch 80/100
625/625 [==============================] - ETA: 0s - loss: 0.7152 - accuracy: 0.8250
Epoch 80: val_loss improved from 1.06485 to 1.06066, saving model to model

625/625 [==============================] - 1692s 3s/step - loss: 0.7152 - accuracy: 0.8250 - val_loss: 1.0607 - val_accuracy: 0.7942
Epoch 81/100
625/625 [==============================] - ETA: 0s - loss: 0.7088 - accuracy: 0.8258
Epoch 81: val_loss improved from 1.06066 to 1.05814, saving model to model

625/625 [==============================] - 1620s 3s/step - loss: 0.7088 - accuracy: 0.8258 - val_loss: 1.0581 - val_accuracy: 0.7955
Epoch 82/100
625/625 [==============================] - ETA: 0s - loss: 0.7011 - accuracy: 0.8271
Epoch 82: val_loss improved from 1.05814 to 1.05651, saving model to model

625/625 [==============================] - 1587s 3s/step - loss: 0.7011 - accuracy: 0.8271 - val_loss: 1.0565 - val_accuracy: 0.7957
Epoch 83/100
625/625 [==============================] - ETA: 0s - loss: 0.6960 - accuracy: 0.8278
Epoch 83: val_loss improved from 1.05651 to 1.05408, saving model to model

625/625 [==============================] - 1705s 3s/step - loss: 0.6960 - accuracy: 0.8278 - val_loss: 1.0541 - val_accuracy: 0.7956
Epoch 84/100
625/625 [==============================] - ETA: 0s - loss: 0.6901 - accuracy: 0.8287
Epoch 84: val_loss improved from 1.05408 to 1.04978, saving model to model

625/625 [==============================] - 1608s 3s/step - loss: 0.6901 - accuracy: 0.8287 - val_loss: 1.0498 - val_accuracy: 0.7963
Epoch 85/100
625/625 [==============================] - ETA: 0s - loss: 0.6834 - accuracy: 0.8302
Epoch 85: val_loss improved from 1.04978 to 1.04747, saving model to model

625/625 [==============================] - 1574s 3s/step - loss: 0.6834 - accuracy: 0.8302 - val_loss: 1.0475 - val_accuracy: 0.7970
Epoch 86/100
625/625 [==============================] - ETA: 0s - loss: 0.6773 - accuracy: 0.8313
Epoch 86: val_loss improved from 1.04747 to 1.04691, saving model to model

625/625 [==============================] - 1609s 3s/step - loss: 0.6773 - accuracy: 0.8313 - val_loss: 1.0469 - val_accuracy: 0.7963
Epoch 87/100
625/625 [==============================] - ETA: 0s - loss: 0.6718 - accuracy: 0.8319
Epoch 87: val_loss improved from 1.04691 to 1.04322, saving model to model

625/625 [==============================] - 1656s 3s/step - loss: 0.6718 - accuracy: 0.8319 - val_loss: 1.0432 - val_accuracy: 0.7971
Epoch 88/100
625/625 [==============================] - ETA: 0s - loss: 0.6663 - accuracy: 0.8327
Epoch 88: val_loss improved from 1.04322 to 1.04013, saving model to model

625/625 [==============================] - 1749s 3s/step - loss: 0.6663 - accuracy: 0.8327 - val_loss: 1.0401 - val_accuracy: 0.7982
Epoch 89/100
625/625 [==============================] - ETA: 0s - loss: 0.6603 - accuracy: 0.8335
Epoch 89: val_loss improved from 1.04013 to 1.03667, saving model to model

625/625 [==============================] - 1793s 3s/step - loss: 0.6603 - accuracy: 0.8335 - val_loss: 1.0367 - val_accuracy: 0.7981
Epoch 90/100
625/625 [==============================] - ETA: 0s - loss: 0.6550 - accuracy: 0.8348
Epoch 90: val_loss improved from 1.03667 to 1.03568, saving model to model

625/625 [==============================] - 1884s 3s/step - loss: 0.6550 - accuracy: 0.8348 - val_loss: 1.0357 - val_accuracy: 0.7992
Epoch 91/100
104/625 [===>..........................] - ETA: 23:58 - loss: 0.6348 - accuracy: 0.8396

5. 测试

English: that's another issue 
Original Franch: c'est une autre affaire 
Predicted Franch:  c'est une autre affaire


English: we should cut our losses 
Original Franch: nous devrions compenser nos pertes 
Predicted Franch:  nous devrions nous arrêter de nos oreilles


English: jump across 
Original Franch: saute de l'autre côté 
Predicted Franch:  fais de l'autre côté


English: i thought i heard a voice 
Original Franch: j'ai cru entendre une voix 
Predicted Franch:  je pensais entendre une voix


English: let's not go 
Original Franch: n'y allons pas 
Predicted Franch:  ne partons pas


English: does this ring a bell 
Original Franch: ça vous évoque quelque chose 
Predicted Franch:  cela t'évoque quelque chose


English: tom is presumptuous 
Original Franch: tom est présomptueux 
Predicted Franch:  tom est sensible


English: for here or to go 
Original Franch: c'est pour ici ou à emporter  
Predicted Franch:  sur place ou à emporter


English: do what you think is best 
Original Franch: fais ce que tu penses être le mieux 
Predicted Franch:  fais ce que tu penses que c'est mieux


English: i was dazzled 
Original Franch: j'étais éblouie 
Predicted Franch:  j'étais en train de nettoyer


English: i doubt it 
Original Franch: j'en doute 
Predicted Franch:  j'en doute


English: he was sentenced to death 
Original Franch: il fut condamné à mort 
Predicted Franch:  il a été condamné à mort


English: i'm totally confused 
Original Franch: je suis complètement perdue 
Predicted Franch:  je suis complètement confus


English: that wasn't my question 
Original Franch: ce n'était pas ma question 
Predicted Franch:  ce n'était pas ma question


English: what is that smell 
Original Franch: c’est quoi cette odeur 
Predicted Franch:  qu'est ce que ça a t il de l'importance


English: we need an answer 
Original Franch: il nous faut une réponse 
Predicted Franch:  il nous faut une réponse


English: she adores him 
Original Franch: elle le vénère 
Predicted Franch:  elle l'aime


English: they must be fake 
Original Franch: ils doivent être faux 
Predicted Franch:  ils doivent être faux


English: he is mentally handicapped 
Original Franch: il est handicapé mental 
Predicted Franch:  il est américain


English: i'm here by choice 
Original Franch: je suis ici de mon plein gré 
Predicted Franch:  je suis là tout seul


English: tom looks determined 
Original Franch: tom semble déterminé 
Predicted Franch:  tom a l'air déterminé


English: this pen has run dry 
Original Franch: ce stylo n'a plus d'encre 
Predicted Franch:  ce stylo a cessé de lait


English: how did you get hurt 
Original Franch: comment vous êtes vous blessé 
Predicted Franch:  comment t'es tu blessé


English: i'm no dummy 
Original Franch: je ne suis pas abrutie 
Predicted Franch:  je ne suis pas idiot


English: we all cried a lot 
Original Franch: nous avons toutes beaucoup pleuré 
Predicted Franch:  nous avons toutes beaucoup pleuré


English: i've found a better way 
Original Franch: j'ai trouvé un meilleur moyen 
Predicted Franch:  j'ai trouvé un meilleur moyen


English: sorry we're full today 
Original Franch: désolé nous sommes complets aujourd'hui 
Predicted Franch:  désolé nous sommes très occupés aujourd'hui


English: did they say anything 
Original Franch: ont ils dit quelque chose 
Predicted Franch:  ont elles dit quoi que ce soit


English: have you ever been in love 
Original Franch: avez vous jamais été amoureux 
Predicted Franch:  as tu jamais été amoureux


English: here take it 
Original Franch: tiens prends le 
Predicted Franch:  voilà le


English: the wind blew all day 
Original Franch: le vent a soufflé toute la journée 
Predicted Franch:  le vent s'est éclairci toute la journée


English: don't make me choose 
Original Franch: ne m'obligez pas à choisir 
Predicted Franch:  ne m'obligez pas à faire de mes amis


English: we walked to my room 
Original Franch: nous avons marché jusqu'à ma chambre 
Predicted Franch:  nous sommes restés chez moi


English: give it some thought 
Original Franch: réfléchis y un peu 
Predicted Franch:  donne le à le croire


English: i opened the window 
Original Franch: j'ouvris la fenêtre 
Predicted Franch:  j'ai fermé la fenêtre


English: tom is awkward 
Original Franch: tom est maladroit 
Predicted Franch:  tom est blessé


English: i can't wait till summer 
Original Franch: je ne peux pas attendre jusqu'à l'été 
Predicted Franch:  je ne peux pas attendre jusqu'à l'été


English: i'm not lazy 
Original Franch: je ne suis pas paresseux 
Predicted Franch:  je ne suis pas paresseux


English: it's no big deal 
Original Franch: ça n’est pas grave 
Predicted Franch:  ce n'est pas un grand


English: i never was shy 
Original Franch: je n'ai jamais été timide 
Predicted Franch:  je n'ai jamais été timide


English: i need to impress tom 
Original Franch: je dois impressionner tom 
Predicted Franch:  j'ai besoin de tom


English: i want to see your boss 
Original Franch: je veux voir ton patron 
Predicted Franch:  je veux voir ton patron


English: the wind blew all day 
Original Franch: le vent souffla tout le jour 
Predicted Franch:  le vent s'est éclairci toute la journée


English: that umbrella is tom's 
Original Franch: ce parapluie est à tom 
Predicted Franch:  ce parapluie est tom


English: do you mean what you say 
Original Franch: tu dis ça sérieusement  
Predicted Franch:  tu comprends ce que je veux dire