Hi
I have this issue when I am runing my app:
NotImplementedError: Cannot convert a symbolic Tensor (lstm/strided_slice:0) to a numpy array
I do not know what is the problem.
Thanks for yout help
Hi
I have this issue when I am runing my app:
NotImplementedError: Cannot convert a symbolic Tensor (lstm/strided_slice:0) to a numpy array
I do not know what is the problem.
Thanks for yout help
Hi @Manu_Soria,
Welcome to our forum!
Itโs difficult for me to accurately answer your question without more context. However, one potential solution to consider is using the .numpy()
method on a TensorFlow tensor.
To provide a better, more accurate response, it would be helpful to have more information abotu the specific operation that is causing the error.
I hope this helps,
Charly
Thanks for your reply and you are right, I should give more information, so:
I developed this app, and the first time it was running ok in my laptop.
The streamlit cloud is running ok.
After I did a Anaconda upgrade in my laptop and appeared the error that I wrote.
My libraries:
import math
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM
import matplotlib.pyplot as plt
My LSTM code:
data = df.filter([โ$/Toneladaโ])
#data
#Converting the dataframe to a numpy array
dataset = data.values
#dataset
#Get /Compute the number of rows to train the model on
training_data_len = math.ceil( len(dataset) *.8)
#training_data_len
#Scale the all of the data to be values between 0 and 1
scaler = MinMaxScaler(feature_range=(0, 1))
#scaler
scaled_data = scaler.fit_transform(dataset)
#scaled_data
#Create the scaled training data set
train_data = scaled_data[0:training_data_len, : ]
#train_data.shape
#Split the data into x_train and y_train data sets
x_train=
y_train =
for i in range(60,len(train_data)):
x_train.append(train_data[i-60:i,0])
y_train.append(train_data[i,0])
#Scale the all of the data to be values between 0 and 1
scaler = MinMaxScaler(feature_range=(0, 1))
#scaler
#Convert x_train and y_train to numpy arrays
x_train, y_train = np.array(x_train), np.array(y_train)
#x_train.shape, y_train.shape
#Reshape the data into the shape accepted by the LSTM
x_train = np.reshape(x_train, (x_train.shape[0],x_train.shape[1],1))
model = Sequential() #
model.add(LSTM(units=150, return_sequences=True,input_shape=(x_train.shape[1],1)))
#model.add(LSTM(units=25, return_sequences=True))
#model.add(LSTM(units=25, return_sequences=True))
model.add(LSTM(units=150, return_sequences=False))
model.add(Dense(units=150))
model.add(Dense(units=1))
pip versions:
numpy 1.21.6
tensor 0.3.6
tensorboard 2.11.0
tensorboard-data-server 0.6.1
tensorboard-plugin-wit 1.6.0.post3
tensorboardX 2.5.1
tensorflow 2.0.1
tensorflow-estimator 2.0.1
tensorrec 0.26.2
I would appreciate your help