<jupyter_start><jupyter_text>Deep Learning Model<jupyter_code>model = Sequential()
model.add(Dense(30, activation = "relu", input_shape = (4,)))
model.add(Dense(10, activation = "relu"))
model.add(Dense(60, activation = "relu"))
model.add(Dense(40, activation = "relu"))


### Output Layer...
model.add(Dense(3, activation = "softmax"))

model.compile(loss="categorical_crossentropy", optimizer = "adam", metrics=["accuracy"])
H = model.fit(trainX, trainY, validation_split=0.3, epochs=50, batch_size = len(trainY))
loss = H.history["loss"]
val_loss = H.history["val_loss"]
accuracy = H.history["accuracy"]
val_accuracy = H.history["val_accuracy"]
import pandas as pd
import matplotlib.pyplot as plt
tva = pd.DataFrame({
    "Ta":accuracy,
    "Va":val_accuracy
})
tva.boxplot()
plt.show()
model.predict_classes(testX[1:2])
testY[1:2]
loss, Acc = model.evaluate(testX, testY)
Acc<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.17381738173817382, 'fraction_numerical': 0.019801980198019802, 'mean_word_length': 7.053097345132743, 'pattern_counts': {'":': 2, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '47453', 'license': 'MIT', 'n_tokens_mistral': 356, 'n_tokens_neox': 342, 'n_words': 63, 'path': 'Deep Learning with IRIS DataSet.ipynb', 'repo_name': 'Ashish-Dhage7/Deep_Learning_Codes', 'size': '909'}