Upload main.py

main.py

@@ -0,0 +1,51 @@
+import numpy as np
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers
+
+def model(input_shape):
+
+    x_input = layers.Input(shape = input_shape)
+
+    x = layers.Conv1D(196, kernel_size=15, strides=4)(x_input)
+    x = layers.BatchNormalization()(x)
+    x = layers.Activation('relu')(x)
+    x = layers.Dropout(0.8)(x)
+
+    x = layers.GRU(units = 128, return_sequences = True)(x)
+    x = layers.Dropout(0.8)(x)
+    x = layers.BatchNormalization()(x)
+    
+    x = layers.GRU(units = 128, return_sequences = True)(x)
+    x = layers.Dropout(0.8)(x)
+    x = layers.BatchNormalization()(x)
+    x = layers.Dropout(0.8)(x)
+    
+    x = layers.TimeDistributed(layers.Dense(1, activation = "sigmoid"))(x)
+    model = keras.Model(inputs = x_input, outputs = x)
+    
+    return model  
+
+
+#model = model(input_shape = (Tx, n_freq))
+model = model(input_shape = (5511, 101))
+model.summary()
+
+opt = keras.optimizers.Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, decay=0.01)
+model.compile(loss='binary_crossentropy', optimizer=opt, metrics=["accuracy"])
+
+X = np.load("./Data/XY_train/X.npy")
+Y = np.load("./Data/XY_train/Y.npy")
+
+X_dev = np.load("./Data/XY_dev/X_dev.npy")
+Y_dev = np.load("./Data/XY_dev/Y_dev.npy")
+
+model.fit(X, Y, batch_size = 64, epochs=20000)
+
+# save model
+model.save('my_model.h5')  # creates a HDF5 file 'my_model.h5'
+# del model  # deletes the existing model
+
+# returns a compiled model
+# identical to the previous one
+# model = load_model('my_model.h5')