import numpy as np import tensorflow as tf # Step 1: Data Preparation # Assume X_train and y_train are the training data # Assume X_test and y_test are the test data # Step 2: RNN Architecture Definition model = tf.keras.Sequential([ tf.keras.layers.SimpleRNN(units=32, input_shape=(X_train.shape[1], X_train.shape[2])), tf.keras.layers.Dense(units=1, activation='sigmoid') ]) # Step 3: Model Compilation model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])import numpy as np import tensorflow as tf # Step..ALL
Developer User10287|Date and time 23/10/21 19:22:38 Build time 1.576 sec|Game capacity 0.032KB|Script
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