import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM
from tensorflow.keras.layers import Dropout, BatchNormalization,Dense
# to read the csv, please download here.
# Training data
# https://drive.google.com/file/d/1l2JEaxxpmYXxWjKxsEYRn4zbJhc0EZpf/view?usp=sharing
# Testing data
# https://drive.google.com/file/d/1ajUGJ1X338kOjm4ZNwRGV60xiZ9bgMq2/view?usp=sharing
# If you are using colab, after download it, please upload to the working directionary on the left.
# Then you can run the code to read_csv
# If you are using local computer, please modify the file path below.


dataset_train = pd.read_csv('Google_Stock_Price_Train.csv')
dataset_train.head()

#!pip install --upgrade tensorflow

#keras only takes numpy array
training_set = dataset_train.iloc[:, 1: 2].values
training_set.shape
plt.figure(figsize=(6,3))
plt.plot(dataset_train['Open'])
plt.title("Google Stock Open Prices")
plt.xlabel("Time (oldest -> latest)")
plt.ylabel("Stock Open Price")
plt.show()

sc = MinMaxScaler(feature_range = (0, 1))
#fit: get min/max of train data
training_set_scaled = sc.fit_transform(training_set)
print(training_set.shape)
print(training_set_scaled.shape)

(1509, 1)
(1509, 1)

## 60 timesteps and 1 output
steps = 60
X_train = []
y_train = []
for i in range(steps, len(training_set_scaled)):
    X_train.append(training_set_scaled[i-steps: i, 0])
    y_train.append(training_set_scaled[i, 0])

X_train, y_train = np.array(X_train), np.array(y_train)
X_train = np.reshape(X_train, newshape =(X_train.shape[0], X_train.shape[1], 1))
X_train.shape

(1449, 60, 1)

dataset_test = pd.read_csv('Google_Stock_Price_Test.csv')
dataset_test.head()
#keras only takes numpy array
real_stock_price = dataset_test.iloc[:, 1: 2].values
real_stock_price.shape
#vertical concat use 0, horizontal uses 1
dataset_total = pd.concat((dataset_train['Open'], dataset_test['Open']),
                          axis = 0)
##use .values to make numpy array
inputs = dataset_total[len(dataset_total) - len(dataset_test) - steps:].values
#reshape data to only have 1 col
inputs = inputs.reshape(-1, 1)

#scale input
inputs = sc.transform(inputs)

X_test = []
for i in range(steps, len(inputs)):
    X_test.append(inputs[i-steps:i, 0])
X_test = np.array(X_test)
#add dimension of indicator
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
print(X_test.shape)

(125, 60, 1)

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dropout, Dense, ReLU

# Initialize the Sequential model
model = Sequential()

# Add 1st LSTM layer (return_sequences=True)
model.add(LSTM(units=52, return_sequences=True, input_shape=(X_train.shape[1], 1),
        activation='relu',           # Activation function for output gate
         recurrent_activation='sigmoid'))
#model.add(Dropout(rate=0.3))

# Add 2nd LSTM layer (return_sequences=True)
model.add(LSTM(units=16, return_sequences=False,activation='relu',           # Activation function for output gate
         recurrent_activation='sigmoid'))
#model.add(Dropout(rate=0.3))

# Add 3rd LSTM layer (return_sequences=False, since it's the last LSTM layer)
#regressor.add(LSTM(units=64, return_sequences=False))
#regressor.add(Dropout(rate=0.2))

# Add output layer (Dense layer with one output for regression)
#regressor.add(Dense(units=6))
model.add(Dense(units=1, activation='relu'))

# Compile the model
model.compile(optimizer='adam', loss='mean_squared_error')

# Summary of the model
print(model.summary())
model.fit(x = X_train, y = y_train, batch_size = 32, epochs = 30,validation_split=0.2)
predicted_stock_price = model.predict(X_test)
#inverse the scaled value
predicted_stock_price = sc.inverse_transform(predicted_stock_price)

/usr/local/lib/python3.10/dist-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(**kwargs)

Model: "sequential"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm (LSTM)                          │ (None, 60, 52)              │          11,232 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_1 (LSTM)                        │ (None, 16)                  │           4,416 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense (Dense)                        │ (None, 1)                   │              17 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘

 Total params: 15,665 (61.19 KB)

 Trainable params: 15,665 (61.19 KB)

 Non-trainable params: 0 (0.00 B)

None
Epoch 1/30
37/37 ━━━━━━━━━━━━━━━━━━━━ 6s 73ms/step - loss: 0.0750 - val_loss: 0.0018
Epoch 2/30
37/37 ━━━━━━━━━━━━━━━━━━━━ 4s 58ms/step - loss: 0.0014 - val_loss: 0.0083
Epoch 3/30
37/37 ━━━━━━━━━━━━━━━━━━━━ 3s 85ms/step - loss: 6.4948e-04 - val_loss: 0.0042
Epoch 4/30
37/37 ━━━━━━━━━━━━━━━━━━━━ 4s 51ms/step - loss: 6.4922e-04 - val_loss: 0.0037
Epoch 5/30
37/37 ━━━━━━━━━━━━━━━━━━━━ 2s 54ms/step - loss: 5.3546e-04 - val_loss: 0.0029
Epoch 6/30
37/37 ━━━━━━━━━━━━━━━━━━━━ 2s 52ms/step - loss: 5.1008e-04 - val_loss: 0.0055
Epoch 7/30
37/37 ━━━━━━━━━━━━━━━━━━━━ 2s 61ms/step - loss: 5.2052e-04 - val_loss: 0.0019
Epoch 8/30
37/37 ━━━━━━━━━━━━━━━━━━━━ 3s 81ms/step - loss: 5.4289e-04 - val_loss: 0.0017
Epoch 9/30
37/37 ━━━━━━━━━━━━━━━━━━━━ 2s 50ms/step - loss: 5.3451e-04 - val_loss: 0.0029
Epoch 10/30
37/37 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - loss: 4.9438e-04 - val_loss: 0.0047
Epoch 11/30
37/37 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - loss: 5.5105e-04 - val_loss: 0.0011
Epoch 12/30
37/37 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - loss: 3.8230e-04 - val_loss: 0.0014
Epoch 13/30
37/37 ━━━━━━━━━━━━━━━━━━━━ 2s 50ms/step - loss: 4.8923e-04 - val_loss: 8.4947e-04
Epoch 14/30
37/37 ━━━━━━━━━━━━━━━━━━━━ 3s 79ms/step - loss: 4.6211e-04 - val_loss: 0.0012
Epoch 15/30
37/37 ━━━━━━━━━━━━━━━━━━━━ 4s 59ms/step - loss: 3.9790e-04 - val_loss: 9.5497e-04
Epoch 16/30
37/37 ━━━━━━━━━━━━━━━━━━━━ 2s 56ms/step - loss: 5.8368e-04 - val_loss: 0.0011
Epoch 17/30
37/37 ━━━━━━━━━━━━━━━━━━━━ 2s 48ms/step - loss: 3.8443e-04 - val_loss: 0.0015
Epoch 18/30
37/37 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - loss: 3.5271e-04 - val_loss: 0.0042
Epoch 19/30
37/37 ━━━━━━━━━━━━━━━━━━━━ 3s 79ms/step - loss: 5.1585e-04 - val_loss: 7.0276e-04
Epoch 20/30
37/37 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - loss: 4.1841e-04 - val_loss: 6.4166e-04
Epoch 21/30
37/37 ━━━━━━━━━━━━━━━━━━━━ 2s 50ms/step - loss: 4.5032e-04 - val_loss: 9.9880e-04
Epoch 22/30
37/37 ━━━━━━━━━━━━━━━━━━━━ 2s 52ms/step - loss: 3.9691e-04 - val_loss: 0.0017
Epoch 23/30
37/37 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - loss: 3.9491e-04 - val_loss: 6.4021e-04
Epoch 24/30
37/37 ━━━━━━━━━━━━━━━━━━━━ 3s 49ms/step - loss: 3.1938e-04 - val_loss: 0.0011
Epoch 25/30
37/37 ━━━━━━━━━━━━━━━━━━━━ 3s 78ms/step - loss: 3.4534e-04 - val_loss: 0.0011
Epoch 26/30
37/37 ━━━━━━━━━━━━━━━━━━━━ 4s 49ms/step - loss: 3.7845e-04 - val_loss: 5.1618e-04
Epoch 27/30
37/37 ━━━━━━━━━━━━━━━━━━━━ 3s 48ms/step - loss: 3.4205e-04 - val_loss: 5.2175e-04
Epoch 28/30
37/37 ━━━━━━━━━━━━━━━━━━━━ 3s 48ms/step - loss: 3.4234e-04 - val_loss: 4.3003e-04
Epoch 29/30
37/37 ━━━━━━━━━━━━━━━━━━━━ 3s 68ms/step - loss: 3.3639e-04 - val_loss: 4.4719e-04
Epoch 30/30
37/37 ━━━━━━━━━━━━━━━━━━━━ 3s 73ms/step - loss: 3.1749e-04 - val_loss: 0.0012
4/4 ━━━━━━━━━━━━━━━━━━━━ 1s 212ms/step

fig = plt.figure(figsize=(12, 5))
ax1 = plt.subplot2grid((1, 2), (0, 0))  # 1 row, 2 columns, position (0, 0)
ax1.plot(real_stock_price, color='red', label='Real Price')
ax1.plot(predicted_stock_price, color='blue', label='Predicted Price')
ax1.set_title('Google Price Prediction (Time Series)')
ax1.set_xlabel('Time')
ax1.set_ylabel('Price')
ax1.legend()
ax1.grid(True)
x = np.linspace(900, 1200, 100)
ax2 = plt.subplot2grid((1, 2), (0, 1))  # 1 row, 2 columns, position (0, 1)
ax2.plot(x, x, color='red', linestyle='-', label='Equivalent Line')
ax2.plot(real_stock_price, predicted_stock_price, 'b.', label='Pred. vs. Obs.')
ax2.set_title('Google Price Prediction (Scatter)')
ax2.set_xlabel('Observed')
ax2.set_ylabel('Predicted')
ax2.set_aspect('equal')
ax2.set_xlim([900, 1200])
ax2.set_ylim([900, 1200])
ax2.grid(True)
ax2.legend()
plt.tight_layout()
plt.show()

from tensorflow.keras.layers import SimpleRNN, BatchNormalization

# Create RNN model
model_RNN = Sequential([
    SimpleRNN(52, return_sequences=True, input_shape=(X_train.shape[1], 1),
              activation='relu'),  # RNN Layer 1                                          # Batch Normalization
    SimpleRNN(16, return_sequences=False),                                                  # RNN Layer 2                                     # Batch Normalization
    Dense(1, activation='relu')                                   # Output Layer
])

# Compile the model
model_RNN.compile(optimizer='adam', loss='mse')
print(model_RNN.summary())
model_RNN.fit(x = X_train, y = y_train,batch_size=32,epochs=50,validation_split=0.2)

/usr/local/lib/python3.10/dist-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(**kwargs)

Model: "sequential_1"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ simple_rnn (SimpleRNN)               │ (None, 60, 52)              │           2,808 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ simple_rnn_1 (SimpleRNN)             │ (None, 16)                  │           1,104 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_1 (Dense)                      │ (None, 1)                   │              17 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘

 Total params: 3,929 (15.35 KB)

 Trainable params: 3,929 (15.35 KB)

 Non-trainable params: 0 (0.00 B)

None
Epoch 1/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 4s 41ms/step - loss: 0.0170 - val_loss: 0.0176
Epoch 2/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 1s 27ms/step - loss: 9.6354e-04 - val_loss: 0.0066
Epoch 3/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 1s 28ms/step - loss: 3.7462e-04 - val_loss: 0.0061
Epoch 4/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 1s 28ms/step - loss: 3.5291e-04 - val_loss: 0.0051
Epoch 5/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 1s 27ms/step - loss: 3.7763e-04 - val_loss: 0.0040
Epoch 6/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 1s 30ms/step - loss: 2.9151e-04 - val_loss: 0.0030
Epoch 7/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 2s 46ms/step - loss: 2.4055e-04 - val_loss: 0.0025
Epoch 8/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 2s 47ms/step - loss: 3.2479e-04 - val_loss: 0.0035
Epoch 9/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 2s 26ms/step - loss: 2.0474e-04 - val_loss: 0.0017
Epoch 10/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 1s 27ms/step - loss: 1.7320e-04 - val_loss: 0.0043
Epoch 11/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 1s 27ms/step - loss: 2.6413e-04 - val_loss: 0.0027
Epoch 12/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 1s 28ms/step - loss: 2.1953e-04 - val_loss: 0.0024
Epoch 13/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 1s 26ms/step - loss: 2.2870e-04 - val_loss: 0.0019
Epoch 14/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 1s 27ms/step - loss: 1.7741e-04 - val_loss: 0.0020
Epoch 15/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 1s 27ms/step - loss: 1.6928e-04 - val_loss: 0.0018
Epoch 16/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 1s 27ms/step - loss: 1.8081e-04 - val_loss: 0.0020
Epoch 17/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 2s 33ms/step - loss: 1.5466e-04 - val_loss: 0.0015
Epoch 18/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - loss: 1.9036e-04 - val_loss: 0.0036
Epoch 19/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 2s 47ms/step - loss: 1.8925e-04 - val_loss: 0.0021
Epoch 20/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 2s 27ms/step - loss: 1.5765e-04 - val_loss: 0.0021
Epoch 21/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 1s 27ms/step - loss: 1.8670e-04 - val_loss: 0.0014
Epoch 22/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 1s 26ms/step - loss: 1.6140e-04 - val_loss: 0.0019
Epoch 23/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 1s 28ms/step - loss: 1.3598e-04 - val_loss: 0.0028
Epoch 24/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 1s 27ms/step - loss: 1.6182e-04 - val_loss: 0.0013
Epoch 25/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 1s 27ms/step - loss: 1.3415e-04 - val_loss: 9.3002e-04
Epoch 26/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 1s 28ms/step - loss: 1.6832e-04 - val_loss: 9.4082e-04
Epoch 27/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 2s 37ms/step - loss: 1.3766e-04 - val_loss: 0.0016
Epoch 28/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 3s 38ms/step - loss: 1.1091e-04 - val_loss: 0.0022
Epoch 29/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 1s 27ms/step - loss: 1.5975e-04 - val_loss: 9.3129e-04
Epoch 30/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 1s 27ms/step - loss: 1.5195e-04 - val_loss: 0.0024
Epoch 31/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 1s 26ms/step - loss: 1.5389e-04 - val_loss: 0.0017
Epoch 32/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 1s 26ms/step - loss: 1.4026e-04 - val_loss: 0.0020
Epoch 33/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 1s 28ms/step - loss: 1.3248e-04 - val_loss: 0.0015
Epoch 34/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 1s 27ms/step - loss: 1.9398e-04 - val_loss: 8.9390e-04
Epoch 35/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 1s 25ms/step - loss: 1.1298e-04 - val_loss: 0.0014
Epoch 36/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 1s 26ms/step - loss: 1.2752e-04 - val_loss: 7.7109e-04
Epoch 37/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 1s 35ms/step - loss: 1.4149e-04 - val_loss: 8.7161e-04
Epoch 38/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 3s 38ms/step - loss: 1.4476e-04 - val_loss: 0.0015
Epoch 39/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 2s 27ms/step - loss: 1.6538e-04 - val_loss: 9.1392e-04
Epoch 40/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 1s 27ms/step - loss: 1.3523e-04 - val_loss: 5.8130e-04
Epoch 41/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 1s 26ms/step - loss: 1.5506e-04 - val_loss: 6.1110e-04
Epoch 42/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 1s 27ms/step - loss: 1.5385e-04 - val_loss: 0.0011
Epoch 43/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 1s 26ms/step - loss: 1.3727e-04 - val_loss: 0.0014
Epoch 44/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 1s 26ms/step - loss: 1.3262e-04 - val_loss: 7.9966e-04
Epoch 45/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 1s 26ms/step - loss: 1.0460e-04 - val_loss: 9.3541e-04
Epoch 46/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 2s 34ms/step - loss: 1.2252e-04 - val_loss: 0.0010
Epoch 47/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 3s 39ms/step - loss: 1.1470e-04 - val_loss: 0.0012
Epoch 48/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 2s 27ms/step - loss: 1.1387e-04 - val_loss: 5.4329e-04
Epoch 49/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 1s 27ms/step - loss: 1.2072e-04 - val_loss: 0.0014
Epoch 50/50
37/37 ━━━━━━━━━━━━━━━━━━━━ 1s 27ms/step - loss: 1.5341e-04 - val_loss: 0.0011

<keras.src.callbacks.history.History at 0x78774de83040>

predicted_stock_price_RNN = model_RNN.predict(X_test)
predicted_stock_price_RNN = sc.inverse_transform(predicted_stock_price_RNN)

fig = plt.figure(figsize=(12, 5))
ax1 = plt.subplot2grid((1, 2), (0, 0))  # 1 row, 2 columns, position (0, 0)
ax1.plot(real_stock_price, color='red', label='Real Price')
ax1.plot(predicted_stock_price_RNN, color='blue', label='Predicted Price')
ax1.set_title('Google Price Prediction (Time Series)')
ax1.set_xlabel('Time')
ax1.set_ylabel('Price')
ax1.legend()
ax1.grid(True)
x = np.linspace(900, 1200, 100)
ax2 = plt.subplot2grid((1, 2), (0, 1))  # 1 row, 2 columns, position (0, 1)
ax2.plot(x, x, color='red', linestyle='-', label='Equivalent Line')
ax2.plot(real_stock_price, predicted_stock_price_RNN, 'b.', label='Pred. vs. Obs.')
ax2.set_title('Google Price Prediction (Scatter)')
ax2.set_xlabel('Observed')
ax2.set_ylabel('Predicted')
ax2.set_aspect('equal')
ax2.set_xlim([900, 1200])
ax2.set_ylim([900, 1200])
ax2.grid(True)
ax2.legend()
plt.tight_layout()
plt.show()

4/4 ━━━━━━━━━━━━━━━━━━━━ 1s 92ms/step

# Example with GRU
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import GRU

model_GRU = Sequential([
    GRU(32, return_sequences=True, input_shape=(X_train.shape[1], 1)),
    #BatchNormalization(),
    GRU(16),
    #BatchNormalization(),
    Dense(1, activation='relu')
])

# Compile the model
model_GRU.compile(optimizer='adam', loss='mse')
print(model_GRU.summary())
model_GRU.fit(x = X_train, y = y_train,batch_size=32,epochs=20,validation_split=0.2)

/usr/local/lib/python3.10/dist-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(**kwargs)

Model: "sequential_2"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ gru (GRU)                            │ (None, 60, 32)              │           3,360 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ gru_1 (GRU)                          │ (None, 16)                  │           2,400 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_2 (Dense)                      │ (None, 1)                   │              17 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘

 Total params: 5,777 (22.57 KB)

 Trainable params: 5,777 (22.57 KB)

 Non-trainable params: 0 (0.00 B)

None
Epoch 1/20
37/37 ━━━━━━━━━━━━━━━━━━━━ 8s 96ms/step - loss: 0.0205 - val_loss: 0.0071
Epoch 2/20
37/37 ━━━━━━━━━━━━━━━━━━━━ 5s 89ms/step - loss: 8.4116e-04 - val_loss: 4.1760e-04
Epoch 3/20
37/37 ━━━━━━━━━━━━━━━━━━━━ 4s 62ms/step - loss: 2.1220e-04 - val_loss: 3.4530e-04
Epoch 4/20
37/37 ━━━━━━━━━━━━━━━━━━━━ 2s 65ms/step - loss: 2.3977e-04 - val_loss: 4.0680e-04
Epoch 5/20
37/37 ━━━━━━━━━━━━━━━━━━━━ 3s 65ms/step - loss: 2.4798e-04 - val_loss: 3.5080e-04
Epoch 6/20
37/37 ━━━━━━━━━━━━━━━━━━━━ 4s 113ms/step - loss: 2.4314e-04 - val_loss: 3.2878e-04
Epoch 7/20
37/37 ━━━━━━━━━━━━━━━━━━━━ 3s 62ms/step - loss: 2.4331e-04 - val_loss: 4.7494e-04
Epoch 8/20
37/37 ━━━━━━━━━━━━━━━━━━━━ 2s 62ms/step - loss: 1.7080e-04 - val_loss: 2.8601e-04
Epoch 9/20
37/37 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - loss: 1.7629e-04 - val_loss: 5.0249e-04
Epoch 10/20
37/37 ━━━━━━━━━━━━━━━━━━━━ 3s 67ms/step - loss: 1.5528e-04 - val_loss: 2.4207e-04
Epoch 11/20
37/37 ━━━━━━━━━━━━━━━━━━━━ 3s 92ms/step - loss: 2.2187e-04 - val_loss: 2.5356e-04
Epoch 12/20
37/37 ━━━━━━━━━━━━━━━━━━━━ 3s 67ms/step - loss: 2.1749e-04 - val_loss: 3.3062e-04
Epoch 13/20
37/37 ━━━━━━━━━━━━━━━━━━━━ 2s 61ms/step - loss: 1.7155e-04 - val_loss: 4.3853e-04
Epoch 14/20
37/37 ━━━━━━━━━━━━━━━━━━━━ 2s 62ms/step - loss: 1.5625e-04 - val_loss: 4.0721e-04
Epoch 15/20
37/37 ━━━━━━━━━━━━━━━━━━━━ 2s 61ms/step - loss: 1.9203e-04 - val_loss: 3.7330e-04
Epoch 16/20
37/37 ━━━━━━━━━━━━━━━━━━━━ 3s 81ms/step - loss: 1.3395e-04 - val_loss: 2.6560e-04
Epoch 17/20
37/37 ━━━━━━━━━━━━━━━━━━━━ 3s 79ms/step - loss: 1.4167e-04 - val_loss: 1.8545e-04
Epoch 18/20
37/37 ━━━━━━━━━━━━━━━━━━━━ 2s 62ms/step - loss: 1.8913e-04 - val_loss: 3.2222e-04
Epoch 19/20
37/37 ━━━━━━━━━━━━━━━━━━━━ 2s 61ms/step - loss: 1.7410e-04 - val_loss: 2.6995e-04
Epoch 20/20
37/37 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - loss: 1.7524e-04 - val_loss: 3.5745e-04

<keras.src.callbacks.history.History at 0x78774e447400>

predicted_stock_price_GRU = model_GRU.predict(X_test)
predicted_stock_price_GRU = sc.inverse_transform(predicted_stock_price_GRU)

fig = plt.figure(figsize=(12, 5))
ax1 = plt.subplot2grid((1, 2), (0, 0))  # 1 row, 2 columns, position (0, 0)
ax1.plot(real_stock_price, color='red', label='Real Price')
ax1.plot(predicted_stock_price_GRU, color='blue', label='Predicted Price')
ax1.set_title('Google Price Prediction (Time Series)')
ax1.set_xlabel('Time')
ax1.set_ylabel('Price')
ax1.legend()
ax1.grid(True)
x = np.linspace(900, 1200, 100)
ax2 = plt.subplot2grid((1, 2), (0, 1))  # 1 row, 2 columns, position (0, 1)
ax2.plot(x, x, color='red', linestyle='-', label='Equivalent Line')
ax2.plot(real_stock_price, predicted_stock_price_GRU, 'b.', label='Pred. vs. Obs.')
ax2.set_title('Google Price Prediction (Scatter)')
ax2.set_xlabel('Observed')
ax2.set_ylabel('Predicted')
ax2.set_aspect('equal')
ax2.set_xlim([900, 1200])
ax2.set_ylim([900, 1200])
ax2.grid(True)
ax2.legend()
plt.tight_layout()
plt.show()

WARNING:tensorflow:5 out of the last 9 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x78774c245870> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.

1/4 ━━━━━━━━━━━━━━━━━━━━ 1s 338ms/step

WARNING:tensorflow:6 out of the last 12 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x78774c245870> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.

4/4 ━━━━━━━━━━━━━━━━━━━━ 1s 129ms/step

	Date	Open	High	Low	Close	Volume
0	01/03/2012	325.25	332.83	324.97	663.59	7,380,500
1	01/04/2012	331.27	333.87	329.08	666.45	5,749,400
2	01/05/2012	329.83	330.75	326.89	657.21	6,590,300
3	01/06/2012	328.34	328.77	323.68	648.24	5,405,900
4	01/09/2012	322.04	322.29	309.46	620.76	11,688,800

GRU¶

We did't introduced GRU, but it is a improvement of LSTM and RNN.¶

For more about GRU, please refer to:¶

Cho et al., (2014): https://arxiv.org/pdf/1406.1078 ¶

Chung et al. (2015): https://arxiv.org/pdf/1412.3555 ¶

¶

GRU¶

We did't introduced GRU, but it is a improvement of LSTM and RNN.¶

For more about GRU, please refer to:¶

Cho et al., (2014): https://arxiv.org/pdf/1406.1078¶

Chung et al. (2015): https://arxiv.org/pdf/1412.3555¶

¶

Cho et al., (2014): https://arxiv.org/pdf/1406.1078 ¶

Chung et al. (2015): https://arxiv.org/pdf/1412.3555 ¶