I'm just starting with deep learning and python and I'm already stuck with this error when I try to train the model. I thought it would be an easy starting project to get together the basic building blocks, but I obviously haven't grasped some basics.. My goal is to train a model on a data set of 5 column values '1ex','2ex','3ex','4ex','5ex' and predict sequences of 5 values. I'm reading the dataset from a csv file i generated and it's displaying as expected. Can you help me understanding what I'm missing out? Thank you very much as always. This is the code I've written so far: import numpy as np import matplotlib.pyplot as plt import pandas as pd import os from sklearn.preprocessing import MinMaxScaler from collections import deque import random # load the data set df = pd.read_csv('DataSet.csv',delimiter=',',usecols=['Wheel','Date','1ex','2ex','3ex','4ex','5ex']) # divide it into portions times = sorted(df.index.values) # get the times last_10pct = sorted(df.index.values)[-int(0.1*len(times))] # get the last 10% of the times last_20pct = sorted(df.index.values)[-int(0.2*len(times))] # get the last 20% of the times test_df = df[(df.index >= last_10pct)] validation_df = df[(df.index >= last_20pct) & (df.index < last_10pct)] train_df = df[(df.index < last_20pct)] # now the train_df is all the data up to the last 20% # drop 'Date' column train_df.drop(columns=["Date"], inplace=True) validation_df.drop(columns=["Date"], inplace=True) test_df.drop(columns=["Date"], inplace=True) # the model from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense # from tensorflow.keras.layers import LSTM from tensorflow.keras.wrappers.scikit_learn import KerasRegressor from sklearn.model_selection import cross_val_score from sklearn.model_selection import KFold from sklearn.preprocessing import StandardScaler from sklearn.pipeline import Pipeline # define base model def baseline_model(): # scale = StandardScaler() # create model model = Sequential() model.add(Dense(5, input_dim=5, kernel_initializer='normal', activation='relu')) model.add(Dense(15, kernel_initializer='normal', activation='relu')) model.add(Dense(15, kernel_initializer='normal', activation='relu')) model.add(Dense(5, kernel_initializer='normal', activation = softmax)) # Compile model # model.compile(loss='mean_absolute_error', optimizer='adam') model.compile(loss='mean_squared_error', optimizer='adam') # model.fit(train_df, epochs = 5) return model # train the model baseline_model.fit(train_df, batch_size=1, epochs=200, verbose=1) Continue reading...
