def evaluate_model(model):
history = model.fit(x_train,
y_train,
epochs=50,
batch_size=256,
validation_data=(x_valid, y_valid))
plot_acc_loss(history)
def try_conv1d_imdb():
model = Sequential()
model.add(layers.Embedding(max_features, 128, input_length=maxlen))
model.add(layers.Conv1D(32, 7, activation='relu'))
model.add(layers.MaxPooling1D(5))
model.add(layers.Conv1D(32, 7, activation='relu'))
model.add(layers.GlobalMaxPooling1D())
model.add(layers.Dense(1))
model.summary()
model.compile(optimizer=RMSprop(lr=1e-4),
loss='binary_crossentropy',
metrics=['acc'])
history = model.fit(x_train,
y_train,
epochs=8,
batch_size=128,
validation_split=.2)
plot_acc_loss(history, 8)
model.evaluate(x_test, y_test) # .85 acc, .43 loss
testing_sequences = tokenizer.texts_to_sequences(testing_sentences)
testing_padded = pad_sequences(testing_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)
training_padded = np.array(training_padded)
training_labels = np.array(training_labels)
testing_padded = np.array(testing_padded)
testing_labels = np.array(testing_labels)
""" MODEL TUNING """
model = tf.keras.Sequential([
tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),
tf.keras.layers.Bidirectional(tf.keras.layers.GRU(64)),
# tf.keras.layers.Conv1D(128, 5, activation='relu'),
# tf.keras.layers.GlobalMaxPooling1D(),
# tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32, return_sequences=True)),
# tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(16)),
tf.keras.layers.Dense(24, activation='relu'),
tf.keras.layers.Dense(1, activation='sigmoid')
])
# model.summary()
model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['acc'])
history = model.fit(training_padded, training_labels,
epochs=num_epochs, batch_size=128,
validation_data=(testing_padded, testing_labels))
plot_acc_loss(history)
ys = tf.keras.utils.to_categorical(labels, num_classes=total_words)
model = tf.keras.models.Sequential()
model.add(
tf.keras.layers.Embedding(total_words,
100,
input_length=max_sequence_len - 1))
model.add(tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(150)))
model.add(tf.keras.layers.Dense(total_words, activation='softmax'))
model.compile(loss='categorical_crossentropy',
metrics=['acc'],
optimizer='adam')
history = model.fit(xs, ys, epochs=100, verbose=1)
plot_acc_loss(history, val=False)
seed_text = 'Laurence went to dublin'
next_words = 100
for _ in range(next_words):
token_list = tokenizer.texts_to_sequences([seed_text])[0]
token_list = pad_sequences([token_list],
maxlen=max_sequence_len - 1,
padding='pre')
predicted = model.predict_classes(token_list)
output_word = ""
for word, index in tokenizer.word_index.items():
if index == predicted:
output_word = word
break