bhavbhavyaa

embedding_dim = 20
model_lstm_bi = keras.Sequential([
    keras.layers.Embedding(vocab_size, embedding_dim, input_length = maxlen),
    keras.layers.Bidirectional(keras.layers.LSTM(embedding_dim)),
    keras.layers.Dense(16, activation = 'relu'),
    keras.layers.Dense(5),
])

model_lstm_bi.summary()

bhavbhavyaa

input_size = 15 
hidden_layer_size = 50 
model = tf.keras.Sequential([
    tf.keras.layers.Dense(hidden_layer_size, activation='relu'),
    tf.keras.layers.Dense(hidden_layer_size, activation='relu'), 
    tf.keras.layers.Dense(hidden_layer_size, activation='relu'),
    tf.keras.layers.Dense(2, activation='relu'),
    tf.keras.layers.Dense(1, activation = 'sigmoid')

])

bhavbhavyaa

model = Sequential()
model.add(hub_layer)
model.add(Dense(16, activation = "relu"))
model.add(Dense(1, activation = "sigmoid"))

bhavbhavyaa

model = Sequential()
model.add(Conv2D(16,(5,5),input_shape = (256,256,3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size = (4,4)))

model.add(Conv2D(32,(5,5)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size = (4,4)))

model.add(Conv2D(64,(5,5)))

bhavbhavyaa

def generate_text_seq(model,tokenizer,text_seq_length,seed_text,n_words):
    text = []
    for _ in range(n_words):
        encoded = tokenizer.texts_to_sequences([seed_text])[0]
        encoded = pad_sequences([encoded],maxlen = text_seq_length,truncating = 'pre')
        
        y_predict = model.predict_classes(encoded)
        
        predicted_words = " "
        for word,index in tokenizer.word_index.items():

bhavbhavyaa

model = Sequential()
model.add(Embedding(vocab_size,50,input_length = 50))
model.add(LSTM(100, return_sequences = True))
model.add(LSTM(100))
model.add(Dense(100,activation = "relu"))
model.add(Dense(vocab_size,activation = "softmax"))

model.summary()

bhavbhavyaa

def clean(doc):
    tokens = doc.split()
    table = str.maketrans("","",string.punctuation)
    tokens = [w.translate(table) for w in tokens]
    tokens = [word for word in tokens if word.isalpha()]
    tokens = [word.lower() for word in tokens]
    return tokens
    
tokens = clean(data)