stock data to fit for Conv2D - Python tensorflow-CodePudding

I am trying to feed stock data to Conv2D. But ran into dimension problem. I have no idea how to solve it and need help. Below are detailed steps that I have implemented.

I have attached data and code in the following link:

2. Add time step to it by using:

def reshape_data(X, y, period=28):

n_past = period # number of days to look back in the past and compile into a time series
trainX = []
trainY = np.array(y.iloc[n_past:])
trainY = trainY[..., np.newaxis]
for i in range(n_past, len(X)):
    trainX.append(X[i - n_past:i, 0:X.shape[1]])
trainX = np.array(trainX)
return trainX, trainY

Note:

data can be found here

6. fit and run

model.compile(optimizer=Adam(learning_rate=0.01) , metrics="mse", loss='binary_crossentropy')

reduce_lr  = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss',factor=0.5,patience=10,verbose=0,mode='auto',min_delta=0.0002,cooldown=0,min_lr=0.0001)


early_stop = tf.keras.callbacks.EarlyStopping(monitor="val_loss", patience=80, mode="min",  restore_best_weights = True)

history = model.fit(trainX, trainY, epochs=300,
               batch_size= 512, shuffle=False, verbose = 1,
               # validation_data=(testX, testY),
               validation_split=0.2,
               callbacks=[early_stop, reduce_lr] )

7. ERROR

I thought since I have convered the stock into 30,30,1 should looks like a image dataset, which would enable tensorflow to work. But somehow it doesn't

CodePudding user response：

Add two layers after your convolution layer:

model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(1, activation='sigmoid'))

And do not mix up tensorflow.keras and keras. Rather just use tensorflow.keras for everything.