K_Means

happy_sad_prediction/h-or-s_Prediction.py

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+import tensorflow as tf
+import os
+import zipfile
+from os import path, getcwd, chdir
+
+path = f"{getcwd()}/tmp2/happy-or-sad.zip"
+
+zip_ref = zipfile.ZipFile(path, 'r')
+zip_ref.extractall("/tmp/h-or-s")
+zip_ref.close()
+
+
+# GRADED FUNCTION: train_happy_sad_model
+def train_happy_sad_model():
+    DESIRED_ACCURACY = 0.999
+
+    class myCallback(tf.keras.callbacks.Callback):
+        def on_epoch_end(self, epoch, logs={}):
+            if(logs.get('acc')>0.999):
+                print("\nReached 99.9% accuracy so cancelling training!")
+                self.model.stop_training = True
+
+
+    # This Code Block should Define and Compile the Model. Please assume the images are 150 X 150 in your implementation.
+    model = tf.keras.models.Sequential([
+        # Your Code Here
+         # Note the input shape is the desired size of the image 300x300 with 3 bytes color
+    # This is the first convolution
+        tf.keras.layers.Conv2D(16, (3,3), activation='relu', input_shape=(150, 150, 3)),
+        tf.keras.layers.MaxPooling2D(2, 2),
+    # The second convolution
+        tf.keras.layers.Conv2D(32, (3,3), activation='relu'),
+        tf.keras.layers.MaxPooling2D(2,2),
+    # The third convolution
+        tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
+        tf.keras.layers.MaxPooling2D(2,2),
+    # The fourth convolution
+        tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
+        tf.keras.layers.MaxPooling2D(2,2),
+    # The fifth convolution
+        tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
+        tf.keras.layers.MaxPooling2D(2,2),
+    # Flatten the results to feed into a DNN
+        tf.keras.layers.Flatten(),
+    # 512 neuron hidden layer
+        tf.keras.layers.Dense(512, activation='relu'),
+    # Only 1 output neuron. It will contain a value from 0-1 where 0 for 1 class ('horses') and 1 for the other ('humans')
+        tf.keras.layers.Dense(1, activation='sigmoid')
+    ])
+    callbacks = myCallback()
+    from tensorflow.keras.optimizers import RMSprop
+
+    model.compile(
+              loss='binary_crossentropy',
+              optimizer=RMSprop(lr=0.001),
+              metrics=['acc'])
+
+
+    # This code block should create an instance of an ImageDataGenerator called train_datagen 
+    # And a train_generator by calling train_datagen.flow_from_directory
+
+    from tensorflow.keras.preprocessing.image import ImageDataGenerator
+
+    train_datagen = ImageDataGenerator(rescale=1/255) # Your Code Here  
+
+    # Please use a target_size of 150 X 150.
+    train_generator = train_datagen.flow_from_directory(
+                '/tmp/h-or-s/',  # This is the source directory for training images
+                target_size=(150, 150),  # All images will be resized to 150x150
+                batch_size=128,
+                # Since we use binary_crossentropy loss, we need binary labels
+                class_mode='binary')
+    # Expected output: 'Found 80 images belonging to 2 classes'
+
+    # This code block should call model.fit_generator and train for
+    # a number of epochs.
+    # model fitting
+    history = model.fit_generator(
+            train_generator,
+      steps_per_epoch=8,  
+      epochs=15,
+      verbose=1)
+    # model fitting
+    return history.history['acc'][-1]
+
+train_happy_sad_model()